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Journal articles on the topic 'AI annotation'

Author: Grafiati

Published: 6 June 2025

Last updated: 25 July 2025

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1

Sisodiya, Hariom. "AnnotImage: An Image Annotation App." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35581.

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This paper introduces an AI-driven Android application designed to automate image annotation, addressing the need for efficient labeling across diverse domains. Leveraging deep learning algorithms, the application analyzes images uploaded or captured by users, assigning relevant annotations. Through extensive testing, the application demonstrates high accuracy and efficiency in annotating images, closely matching human annotators' results. The findings underscore the potential of AI-driven annotation tools to revolutionize image dataset creation and utilization, facilitating advancements in ma

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Park, Jinkyung Katie, Rahul Dev Ellezhuthil, Pamela Wisniewski, and Vivek Singh. "Collaborative human-AI risk annotation: co-annotating online incivility with CHAIRA." Information Research an international electronic journal 30, iConf (2025): 992–1008. https://doi.org/10.47989/ir30iconf47146.

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Introduction. Collaborative human-AI annotation is a promising approach for various tasks with large-scale and complex data. Tools and methods to support effective human-AI collaboration for data annotation are an important direction for research. In this paper, we present CHAIRA: a Collaborative Human-AI Risk Annotation tool that enables human and AI agents to collaboratively annotate online incivility. Method. We leveraged Large Language Models (LLMs) to facilitate the interaction between human and AI annotators and examine four different prompting strategies. The developed CHAIRA system com

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Groh, Matthew, Caleb Harris, Roxana Daneshjou, Omar Badri, and Arash Koochek. "Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022): 1–26. http://dx.doi.org/10.1145/3555634.

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While artificial intelligence (AI) holds promise for supporting healthcare providers and improving the accuracy of medical diagnoses, a lack of transparency in the composition of datasets exposes AI models to the possibility of unintentional and avoidable mistakes. In particular, public and private image datasets of dermatological conditions rarely include information on skin color. As a start towards increasing transparency, AI researchers have appropriated the use of the Fitzpatrick skin type (FST) from a measure of patient photosensitivity to a measure for estimating skin tone in algorithmi

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Graëff, Camille, Thomas Lampert, Jean-Paul Mazellier, Nicolas Padoy, Laela El Amiri, and Philippe Liverneaux. "The preliminary stage in developing an artificial intelligence algorithm: a study of the inter- and intra-individual variability of phase annotations in internal fixation of distal radius fracture videos." Artificial Intelligence Surgery 3, no. 3 (2023): 147–59. http://dx.doi.org/10.20517/ais.2023.12.

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Aim: As a preliminary stage in the development of an artificial intelligence (AI) algorithm for surgery, this work aimed to study the inter- and intra-individual variability of phase annotations in videos of minimally invasive plate osteosynthesis of distal radius fractures (MIPO). The main hypothesis was that the inter-individual variability was almost perfect if Cohen's kappa coefficient (k) was ≥ 81% overall; the secondary hypothesis was that the intra-individual variability was almost perfect if the F1-score (F1) was ≥ 81%. Methods: The material comprised 9 annotators and three annotated M

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Pangakis, Nick, and Sam Wolken. "Keeping Humans in the Loop: Human-Centered Automated Annotation with Generative AI." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 1471–92. https://doi.org/10.1609/icwsm.v19i1.35883.

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Automated text annotation is a compelling use case for generative large language models (LLMs) in social media research. Recent work suggests that LLMs can achieve strong performance on annotation tasks; however, these studies evaluate LLMs on a small number of tasks and likely suffer from contamination due to a reliance on public benchmark datasets. Here, we test a human-centered framework for responsibly evaluating artificial intelligence tools used in automated annotation. We use GPT-4 to replicate 27 annotation tasks across 11 password-protected datasets from recently published computation

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Xu, Yixuan, and Jingyi Cui. "Artificial Intelligence in Gene Annotation: Current Applications, Challenges, and Future Prospects." Theoretical and Natural Science 98, no. 1 (2025): 8–15. https://doi.org/10.54254/2753-8818/2025.21464.

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Gene annotation is a critical process in genomics that involves the description of not only the position but also the function of an encoded element of a genome. In general, this provides biological context to sequence data, enabling an advanced level of understanding of genetic information. This is important in areas aligned with genetic engineering, studies of diseases, and evolution. Through ML and DL methodologies, AI enhances functional annotation and gene prediction effectively and accurately. This review focuses on AI in genomic research and assesses its effectiveness compared to tradit

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Grudza, Matthew, Brandon Salinel, Sarah Zeien, et al. "Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training." World Journal of Radiology 15, no. 12 (2023): 359–69. http://dx.doi.org/10.4329/wjr.v15.i12.359.

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BACKGROUND Missing occult cancer lesions accounts for the most diagnostic errors in retrospective radiology reviews as early cancer can be small or subtle, making the lesions difficult to detect. Second-observer is the most effective technique for reducing these events and can be economically implemented with the advent of artificial intelligence (AI). AIM To achieve appropriate AI model training, a large annotated dataset is necessary to train the AI models. Our goal in this research is to compare two methods for decreasing the annotation time to establish ground truth: Skip-slice annotation

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Apud Baca, Javier Gibran, Thomas Jantos, Mario Theuermann, et al. "Automated Data Annotation for 6-DoF AI-Based Navigation Algorithm Development." Journal of Imaging 7, no. 11 (2021): 236. http://dx.doi.org/10.3390/jimaging7110236.

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Accurately estimating the six degree of freedom (6-DoF) pose of objects in images is essential for a variety of applications such as robotics, autonomous driving, and autonomous, AI, and vision-based navigation for unmanned aircraft systems (UAS). Developing such algorithms requires large datasets; however, generating those is tedious as it requires annotating the 6-DoF relative pose of each object of interest present in the image w.r.t. to the camera. Therefore, this work presents a novel approach that automates the data acquisition and annotation process and thus minimizes the annotation eff

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Multusch, Malte Michel, Lasse Hansen, Mattias Paul Heinrich, et al. "Impact of Radiologist Experience on AI Annotation Quality in Chest Radiographs: A Comparative Analysis." Diagnostics 15, no. 6 (2025): 777. https://doi.org/10.3390/diagnostics15060777.

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Background/Objectives: In the burgeoning field of medical imaging and Artificial Intelligence (AI), high-quality annotations for training AI-models are crucial. However, there are still only a few large datasets, as segmentation is time-consuming, experts have limited time. This study investigates how the experience of radiologists affects the quality of annotations. Methods: We randomly collected 53 anonymized chest radiographs. Fifteen readers with varying levels of expertise annotated the anatomical structures of different complexity, pneumonic opacities and central venous catheters (CVC) a

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Yang, Wei-Hua, and Xing-Huai Sun. "Guidelines for glaucoma imaging classification, annotation, and quality control for artificial intelligence applications." International Journal of Ophthalmology 18, no. 7 (2025): 1181–96. https://doi.org/10.18240/ijo.2025.07.01.

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Glaucoma is an eye disease characterized by pathologically elevated intraocular pressure, optic nerve atrophy, and visual field defects, which can lead to irreversible vision loss. In recent years, the rapid development of artificial intelligence (AI) technology has provided new approaches for the early diagnosis and management of glaucoma. By classifying and annotating glaucoma-related images, AI models can learn and recognize the specific pathological features of glaucoma, thereby achieving automated imaging analysis and classification. Research on glaucoma imaging classification and annotat

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Salinel, Brandon, Matthew Grudza, Sarah Zeien, et al. "Comparison of segmentation methods to improve throughput in annotating AI-observer for detecting colorectal cancer." Journal of Clinical Oncology 40, no. 4_suppl (2022): 142. http://dx.doi.org/10.1200/jco.2022.40.4_suppl.142.

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142 Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, and its outcome can be improved with better detection of incidental early CRC on routine CT of the abdomen and pelvis (CTAP). AI-second observer (AI) has the potential as shown in our companion abstract. The bottleneck in training AI is the time required for radiologists to segment the CRC. We compared two techniques for accelerating the segmentation process: 1) Sparse annotation (annotating some of the CT slice containing CRC instead of every slice); 2) Allowing AI to perform initial segmentation fol

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Zheng, Fu, Liu XingMing, Xu JuYing, et al. "Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation." PLOS Digital Health 4, no. 6 (2025): e0000738. https://doi.org/10.1371/journal.pdig.0000738.

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This study investigates the feasibility of reducing manual image annotation costs in medical image database construction by utilizing a step by step approach where the Artificial Intelligence model (AI model) trained on a previous batch of data automatically pre-annotates the next batch of image data, taking ultrasound image of thyroid nodule annotation as an example. The study used YOLOv8 as the AI model. During the AI model training, in addition to conventional image augmentation techniques, augmentation methods specifically tailored for ultrasound images were employed to balance the quantit

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Pehrson, Lea Marie, Dana Li, Alyas Mayar, et al. "Clinicians’ Agreement on Extrapulmonary Radiographic Findings in Chest X-Rays Using a Diagnostic Labelling Scheme." Diagnostics 15, no. 7 (2025): 902. https://doi.org/10.3390/diagnostics15070902.

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Objective: Reliable reading and annotation of chest X-ray (CXR) images are essential for both clinical decision-making and AI model development. While most of the literature emphasizes pulmonary findings, this study evaluates the consistency and reliability of annotations for extrapulmonary findings, using a labelling scheme. Methods: Six clinicians with varying experience levels (novice, intermediate, and experienced) annotated 100 CXR images using a diagnostic labelling scheme, in two rounds, separated by a three-week washout period. Annotation consistency was assessed using Randolph’s free-

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Baur, Tobias, Alexander Heimerl, Florian Lingenfelser, et al. "eXplainable Cooperative Machine Learning with NOVA." KI - Künstliche Intelligenz 34, no. 2 (2020): 143–64. http://dx.doi.org/10.1007/s13218-020-00632-3.

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Abstract In the following article, we introduce a novel workflow, which we subsume under the term “explainable cooperative machine learning” and show its practical application in a data annotation and model training tool called NOVA. The main idea of our approach is to interactively incorporate the ‘human in the loop’ when training classification models from annotated data. In particular, NOVA offers a collaborative annotation backend where multiple annotators join their workforce. A main aspect is the possibility of applying semi-supervised active learning techniques already during the annota

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Hasei, Joe, Ryuichi Nakahara, Yujiro Otsuka, et al. "The Three-Class Annotation Method Improves the AI Detection of Early-Stage Osteosarcoma on Plain Radiographs: A Novel Approach for Rare Cancer Diagnosis." Cancers 17, no. 1 (2024): 29. https://doi.org/10.3390/cancers17010029.

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Background/Objectives: Developing high-performance artificial intelligence (AI) models for rare diseases is challenging owing to limited data availability. This study aimed to evaluate whether a novel three-class annotation method for preparing training data could enhance AI model performance in detecting osteosarcoma on plain radiographs compared to conventional single-class annotation. Methods: We developed two annotation methods for the same dataset of 468 osteosarcoma X-rays and 378 normal radiographs: a conventional single-class annotation (1C model) and a novel three-class annotation met

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Bhanu Teja Reddy Maryala. "Global Ethical AI Data Standard: A framework for regulatory harmonization." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2836–41. https://doi.org/10.30574/wjaets.2025.15.2.0851.

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The Global Ethical AI Data Standard (GEADS) introduces a comprehensive framework addressing fundamental challenges in artificial intelligence data governance. Production AI systems frequently exhibit inadequate provenance documentation and questionable consent mechanisms, while data annotation practices often lack transparency and fair compensation. The regulatory environment presents significant fragmentation across jurisdictions, with limited convergence in core data protection principles between major frameworks like GDPR and CCPA. Informed consent mechanisms fundamentally fail in AI contex

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Cornwell, Peter. "Progress with Repository-based Annotation Infrastructure for Biodiversity Applications." Biodiversity Information Science and Standards 7 (September 14, 2023): e112707. https://doi.org/10.3897/biss.7.112707.

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Rapid development since the 1980s of technologies for analysing texts, has led not only to widespread employment of text 'mining', but also to now-pervasive large language model artificial intelligence (AI) applications. However, building new, concise, data resources from historic, as well as contemporary scientific literature, which can be employed efficiently at scale by automation and which have long-term value for the research community, has proved more elusive.Efforts at codifying analyses, such as the Text Encoding Initiative (TEI), date from the early 1990s and were initially driven by

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Valentine, Melissa A., Roger E. Bohn, Amanda L. Pratt, Prachee Jain, Sara J. Singer, and Michael S. Bernstein. "Constructing a Classification Scheme - and its Consequences: A Field Study of Learning to Label Data for Computer Vision in a Hospital Intensive Care Unit." Proceedings of the ACM on Human-Computer Interaction 8, CSCW2 (2024): 1–29. http://dx.doi.org/10.1145/3687029.

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Research on data annotation for artificial intelligence (AI) has demonstrated that biases, power, and culture impact the ways that annotators apply labels to data and subsequently affect downstream AI systems. However, annotators can only apply labels that are available to them in the annotation classification scheme. Drawing on a 3-year ethnographic study of an R&D collaboration between medical and AI researchers, we argue that the construction of the classification schema itself -- decisions about what kinds of data can and cannot be collected, what activities can and cannot be detected

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Schouten, Gerard, Bas S. H. T. Michielsen, and Barbara Gravendeel. "Data-centric AI approach for automated wildflower monitoring." PLOS ONE 19, no. 9 (2024): e0302958. http://dx.doi.org/10.1371/journal.pone.0302958.

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We present the Eindhoven Wildflower Dataset (EWD) as well as a PyTorch object detection model that is able to classify and count wildflowers. EWD, collected over two entire flowering seasons and expert annotated, contains 2,002 top-view images of flowering plants captured ‘in the wild’ in five different landscape types (roadsides, urban green spaces, cropland, weed-rich grassland, marshland). It holds a total of 65,571 annotations for 160 species belonging to 31 different families of flowering plants and serves as a reference dataset for automating wildflower monitoring and object detection in

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Chandhiramowuli, Srravya, Alex S. Taylor, Sara Heitlinger, and Ding Wang. "Making Data Work Count." Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (2024): 1–26. http://dx.doi.org/10.1145/3637367.

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In this paper, we examine the work of data annotation. Specifically, we focus on the role of counting or quantification in organising annotation work. Based on an ethnographic study of data annotation in two outsourcing centres in India, we observe that counting practices and its associated logics are an integral part of day-to-day annotation activities. In particular, we call attention to the presumption of total countability observed in annotation - the notion that everything, from tasks, datasets and deliverables, to workers, work time, quality and performance, can be managed by applying th

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Lost, Jan, Niklas Tillmans, Sara Merkaj, et al. "NIMG-20. INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET." Neuro-Oncology 24, Supplement_7 (2022): vii165—vii166. http://dx.doi.org/10.1093/neuonc/noac209.638.

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Abstract PURPOSE Translation of AI algorithms into clinical practice is significantly limited by lack of large individual hospital-based datasets with expert annotations. Current methods for generation of annotated imaging data are significantly limited due to inefficient imaging data transfer, complicated annotation software, and time required for experts to generate ground truth information. We incorporated AI tools for auto-segmentation of gliomas into PACS that is used at our institution for reading clinical studies and developed a workflow for annotation of images and development of volum

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Tillmanns, N., J. Lost, S. Merkaj, et al. "P13.05.B INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET." Neuro-Oncology 25, Supplement_2 (2023): ii101. http://dx.doi.org/10.1093/neuonc/noad137.339.

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Abstract BACKGROUND Translation of AI algorithms into clinical practice is significantly limited by lack of large individual hospital-based datasets with expert annotations. Current methods for generation of annotated imaging data are significantly limited due to inefficient imaging data transfer, complicated annotation software, and time required for experts to generate ground truth information. We incorporated AI tools for auto-segmentation of gliomas into PACS that is used at our institution for reading clinical studies and developed a workflow for annotation of images and development of vo

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Li, Yongqi, Xin Miao, Mayi Xu, and Tieyun Qian. "Strong Empowered and Aligned Weak Mastered Annotation for Weak-to-Strong Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 26 (2025): 27437–45. https://doi.org/10.1609/aaai.v39i26.34955.

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The super-alignment problem of how humans can effectively supervise super-human AI has garnered increasing attention. Recent research has focused on investigating the weak-to-strong generalization (W2SG) scenario as an analogy for super-alignment. This scenario examines how a pre-trained strong model, supervised by an aligned weak model, can outperform its weak supervisor. Despite good progress, current W2SG methods face two main issues: 1) The annotation quality is limited by the knowledge scope of the weak model; 2) It is risky to position the strong model as the final corrector. To tackle t

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Kim, Yuna, Ji-Soo Keum, Jie-Hyun Kim, et al. "Real-World Colonoscopy Video Integration to Improve Artificial Intelligence Polyp Detection Performance and Reduce Manual Annotation Labor." Diagnostics 15, no. 7 (2025): 901. https://doi.org/10.3390/diagnostics15070901.

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Background/Objectives: Artificial intelligence (AI) integration in colon polyp detection often exhibits high sensitivity but notably low specificity in real-world settings, primarily due to reliance on publicly available datasets alone. To address this limitation, we proposed a semi-automatic annotation method using real colonoscopy videos to enhance AI model performance and reduce manual labeling labor. Methods: An integrated AI model was trained and validated on 86,258 training images and 17,616 validation images. Model 1 utilized only publicly available datasets, while Model 2 additionally

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Bartolo, Max, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp. "Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension." Transactions of the Association for Computational Linguistics 8 (November 2020): 662–78. http://dx.doi.org/10.1162/tacl_a_00338.

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Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from da

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Addink, Wouter, Sam Leeflang, and Sharif Islam. "A Simple Recipe for Cooking your AI-assisted Dish to Serve it in the International Digital Specimen Architecture." Biodiversity Information Science and Standards 7 (September 14, 2023): e112678. https://doi.org/10.3897/biss.7.112678.

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With the rise of Artificial Intelligence (AI), a large set of new tools and services is emerging that supports specimen data mapping, standards alignment, quality enhancement and enrichment of the data. These tools currently operate in isolation, targeted to individual collections, collection management systems and institutional datasets. To address this challenge, DiSSCo, the Distributed System of Scientific Collections, is developing a new infrastructure for digital specimens, transforming them into actionable information objects. This infrastructure incorporates a framework for annotation a

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Lin, Tai-Pei, Chiou-Ying Yang, Ko-Jiunn Liu, Meng-Yuan Huang, and Yen-Lin Chen. "Immunohistochemical Stain-Aided Annotation Accelerates Machine Learning and Deep Learning Model Development in the Pathologic Diagnosis of Nasopharyngeal Carcinoma." Diagnostics 13, no. 24 (2023): 3685. http://dx.doi.org/10.3390/diagnostics13243685.

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Nasopharyngeal carcinoma (NPC) is an epithelial cancer originating in the nasopharynx epithelium. Nevertheless, annotating pathology slides remains a bottleneck in the development of AI-driven pathology models and applications. In the present study, we aim to demonstrate the feasibility of using immunohistochemistry (IHC) for annotation by non-pathologists and to develop an efficient model for distinguishing NPC without the time-consuming involvement of pathologists. For this study, we gathered NPC slides from 251 different patients, comprising hematoxylin and eosin (H&E) slides, pan-cytok

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Tang, Kun, Xu Cao, Zhipeng Cao, et al. "THMA: Tencent HD Map AI System for Creating HD Map Annotations." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15585–93. http://dx.doi.org/10.1609/aaai.v37i13.26848.

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Nowadays, autonomous vehicle technology is becoming more and more mature. Critical to progress and safety, high-definition (HD) maps, a type of centimeter-level map collected using a laser sensor, provide accurate descriptions of the surrounding environment. The key challenge of HD map production is efficient, high-quality collection and annotation of large-volume datasets. Due to the demand for high quality, HD map production requires significant manual human effort to create annotations, a very time-consuming and costly process for the map industry. In order to reduce manual annotation burde

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Sklab, Youcef, Hanane Ariouat, Youssef Boujydah, et al. "Towards a Deep Learning-Powered Herbarium Image Analysis Platform." Biodiversity Information Science and Standards 8 (August 28, 2024): e135629. https://doi.org/10.3897/biss.8.135629.

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Global digitization efforts have archived millions of specimen scans worldwide in herbarium collections, which are essential for studying plant evolution and biodiversity. ReColNat hosts, at present, over 10 million images. However, analyzing these datasets poses crucial challenges for botanical research. The application of deep learning in biodiversity analyses, particularly in analyzing herbarium scans, has shown promising results across numerous tasks (Ariouat et al. 2023, Ariouat et al. 2024, Groom et al. 2023, Sahraoui et al. 2023).Within the e-Col+project (ANR-21-ESRE-0053), we are devel

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M, Sutharsan. "SMART ANALYSIS OF AUTOMATED AND SEMI-AUTOMATED APPROACHES TO DATA ANNOTATION FOR MACHINE LEARNING." ICTACT Journal on Data Science and Machine Learning 4, no. 3 (2023): 457–60. https://doi.org/10.21917/ijdsml.2023.0106.

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Data annotation for machine learning is the process of labeling data so that machines can properly identify patterns and other related information. It is a critical task within many artificial intelligence (AI) and machine learning (ML) projects. The traditional approach to data annotation involves manual input from a knowledgeable human expert. This, however, can be extremely costly, both in terms of time and money. To help reduce these costs, automated and semi-automated approaches to data annotation have been explored. Automated approaches are computer programs that label data automatically

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Zhang, Kai, Ahmad Elalailyi, Luca Perfetti, and Francesco Fassi. "Cost-effective annotation of fisheye images for object detection." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W8-2024 (December 14, 2024): 491–98. https://doi.org/10.5194/isprs-archives-xlviii-2-w8-2024-491-2024.

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Abstract. Nowadays, fisheye image has become commonly used in the 3D reality capturing field. Although AI integration for image recognition has become mature with normal images, providing available annotated dataset and pre-trained models, its application for fisheye images is rarely seen. While the object detection models have generalization ability, dealing with barrel distortion requires specific data for fine-tuning. This paper seeks to acquire prior knowledge from normal image and transfer it to the application that deal with fisheye images. This research is devoted to test the annotation

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He, Shan, Vakul Mohanty, Yukun Tan, et al. "Abstract B040: Literature-scaled immunological gene set annotation using AI-powered immune cell knowledge graph (ICKG)." Clinical Cancer Research 31, no. 13_Supplement (2025): B040. https://doi.org/10.1158/1557-3265.aimachine-b040.

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Abstract Large scale application of single-cell and spatial omics in models and patient samples has led to the discovery of many novel gene sets, particularly those from an immunotherapeutic contexts. However, the biological meaning of those gene sets has been interpreted anecdotally through over- representation analysis against canonical annotation databases of limited complexity, granularity, and accuracy. Rich functional descriptions of individual genes in an immunological context exist in the literature but are not semantically summarized to perform gene set analysis. To overcome this limi

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Gutierrez Becker, B., E. Giuffrida, M. Mangia, et al. "P069 Artificial intelligence (AI)-filtered Videos for Accelerated Scoring of Colonoscopy Videos in Ulcerative Colitis Clinical Trials." Journal of Crohn's and Colitis 15, Supplement_1 (2021): S173—S174. http://dx.doi.org/10.1093/ecco-jcc/jjab076.198.

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Abstract Background Endoscopic assessment is a critical procedure to assess the improvement of mucosa and response to therapy, and therefore a pivotal component of clinical trial endpoints for IBD. Central scoring of endoscopic videos is challenging and time consuming. We evaluated the feasibility of using an Artificial Intelligence (AI) algorithm to automatically produce filtered videos where the non-readable portions of the video are removed, with the aim of accelerating the scoring of endoscopic videos. Methods The AI algorithm was based on a Convolutional Neural Network trained to perform

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Pennington, Avery, Oliver N. F. King, Win Min Tun, et al. "From Voxels to Viruses: Using Deep Learning and Crowdsourcing to Understand a Virus Factory." Citizen Science: Theory and Practice 9, no. 1 (2024): 37. https://doi.org/10.5334/cstp.739.

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Many bioimaging research projects require objects of interest to be identified, located, and then traced to allow quantitative measurement. Depending on the complexity of the system and imaging, instance segmentation is often done manually, and automated approaches still require weeks to months of an individual’s time to acquire the necessary training data for AI models. As such, there is a strong need to develop approaches for instance segmentation that minimize the use of expert annotation while maintaining quality on challenging image analysis problems. Herein, we present our work on a citi

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Haunss, Sebastian, Jonas Kuhn, Sebastian Padó, et al. "Integrating Manual and Automatic Annotation for the Creation of Discourse Network Data Sets." Politics and Governance 8, no. 2 (2020): 326–39. http://dx.doi.org/10.17645/pag.v8i2.2591.

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This article investigates the integration of machine learning in the political claim annotation workflow with the goal to partially automate the annotation and analysis of large text corpora. It introduces the MARDY annotation environment and presents results from an experiment in which the annotation quality of annotators with and without machine learning based annotation support is compared. The design and setting aim to measure and evaluate: a) annotation speed; b) annotation quality; and c) applicability to the use case of discourse network generation. While the results indicate only sligh

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Matuzevičius, Dalius. "A Retrospective Analysis of Automated Image Labeling for Eyewear Detection Using Zero-Shot Object Detectors." Electronics 13, no. 23 (2024): 4763. https://doi.org/10.3390/electronics13234763.

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This research presents a retrospective analysis of zero-shot object detectors in automating image labeling for eyeglasses detection. The increasing demand for high-quality annotations in object detection is being met by AI foundation models with open-vocabulary capabilities, reducing the need for labor-intensive manual labeling. There is a notable gap in systematic analyses of foundation models for specialized detection tasks, particularly within the domain of facial accessories. Six state-of-the-art models—Grounding DINO, Detic, OWLViT, OWLv2, YOLO World, and Florence-2—were evaluated across

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Qazi, Farheen, Muhammad Naseem, Sonish Aslam, Zainab Attaria, Muhammad Ali Jan, and Syed Salman Junaid. "AnnoVate: Revolutionizing Data Annotation with Automated Labeling Technique." VFAST Transactions on Software Engineering 12, no. 2 (2024): 24–30. http://dx.doi.org/10.21015/vtse.v12i2.1734.

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This research introduces AnnoVate, an innovative web application designed to automate the labor-intensive task of object annotation for computer vision applications. Focused on image annotation, the study addresses the escalating demand for data refinement and labeling in the field of artificial intelligence (AI). Leveraging the power of YOLOv8 (You Only Look Once), a high-performance object detection algorithm, AnnoVate minimizes human intervention while achieving an impressive 85% overall accuracy in object detection. The methodology integrates active learning, allowing labelers to selective

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Lizzi, Francesca, Abramo Agosti, Francesca Brero, et al. "Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria." International Journal of Computer Assisted Radiology and Surgery 17, no. 2 (2021): 229–37. http://dx.doi.org/10.1007/s11548-021-02501-2.

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Abstract Purpose This study aims at exploiting artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. The limited data availability and the annotation quality are relevant factors in training AI-methods. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria. Methods We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net$$_1$$ 1 ) is devoted to the identification of the lung parenchyma; the secon

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Edelmers, Edgars, Dzintra Kazoka, Katrina Bolocko, Kaspars Sudars, and Mara Pilmane. "Automatization of CT Annotation: Combining AI Efficiency with Expert Precision." Diagnostics 14, no. 2 (2024): 185. http://dx.doi.org/10.3390/diagnostics14020185.

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The integration of artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) algorithms, marks a transformative progression in medical imaging diagnostics. This technical note elucidates a novel methodology for semantic segmentation of the vertebral column in CT scans, exemplified by a dataset of 250 patients from Riga East Clinical University Hospital. Our approach centers on the accurate identification and labeling of individual vertebrae, ranging from C1 to the sacrum–coccyx complex. Patient selection was meticulously conducted, ensuring demographic bal

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Pelletier, Mathew G., John D. Wanjura, and Greg A. Holt. "Semi-Automated Training of AI Vision Models." AgriEngineering 7, no. 7 (2025): 225. https://doi.org/10.3390/agriengineering7070225.

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The adoption of AI vision models in specialized industries is often hindered by the substantial requirement for extensive, manually annotated image datasets. Even when employing transfer learning, robust model development typically necessitates tens of thousands of such images, a process that is time-consuming, costly, and demands consistent expert annotation. This technical note introduces a semi-automated method to significantly reduce this annotation burden. The proposed approach utilizes two general-purpose vision-transformer-to-caption (GP-ViTC) models to generate descriptive text from im

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Zubair, Asif, Rich Chapple, Sivaraman Natarajan, et al. "Abstract 456: Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors." Cancer Research 82, no. 12_Supplement (2022): 456. http://dx.doi.org/10.1158/1538-7445.am2022-456.

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Abstract For over 100 years, the traditional tools of pathology, such as tissue-marking dyes (e.g. the H&E stain) have been used to study the disorganization and dysfunction of cells within tissues. This has represented a principal diagnostic and prognostic tool in cancer. However, in the last 5 years, new technologies have promised to revolutionize histopathology, with Spatial Transcriptomics technologies allowing us to measure gene expression directly in pathology-stained tissue sections. In parallel with these developments, Artificial Intelligence (AI) applied to histopathology tissue i

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Santacroce, G., P. Meseguer, I. Zammarchi, et al. "P406 A novel active learning-based digital pathology protocol annotation for histologic assessment in Ulcerative Colitis using PICaSSO Histologic Remission Index (PHRI)." Journal of Crohn's and Colitis 18, Supplement_1 (2024): i843—i844. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0536.

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Abstract Background Histologic remission (HR) is a critical treatment target in Ulcerative Colitis (UC). Among several scoring systems, the PICaSSO Histologic Remission Index (PHRI) simplifies HR assessment by evaluating the presence of neutrophils in the bowel tissue. Our artificial intelligence (AI) system built upon PHRI showed remarkable accuracy in HR assessment. PHRI assess neutrophils in four different regions of interest, so segmentation of these compartments is crucial to predict PHRI automatically. However, creating labelled histopathological datasets to train fully-supervised segmen

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Thakur, Siddhesh, Shahriar Faghani, Mana Moassefi, et al. "TMIC-60. BRATS-PATH: ASSESSING HETEROGENEOUS HISTOPATHOLOGIC REGIONS IN GLIOBLASTOMA." Neuro-Oncology 26, Supplement_8 (2024): viii312. http://dx.doi.org/10.1093/neuonc/noae165.1238.

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Abstract Glioblastoma, the most common malignant primary adult brain tumor, poses significant diagnostic and treatment challenges due to its heterogeneous molecular and micro-environmental profiles. To this end, we organize the BraTS-Path challenge to provide a public benchmarking environment and a comprehensive dataset to develop and validate AI models for identifying distinct histopathologic glioblastoma sub-regions in H&E-stained digitized tissue sections. We identified 188 multi-institutional diagnostic slides of glioblastoma (IDH-wt, Gr.4) cases, from the TCGA-GBM and TCGA-LGG data co

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Sun, Jia, Zheng Wei, and Pan Hui. "Algorithmic Miner: Humanity in Service - An AI-Driven VR Journey into Machine Logic." Proceedings of the ACM on Computer Graphics and Interactive Techniques 8, no. 3 (2025): 1–11. https://doi.org/10.1145/3736784.

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Algorithmic Miner is a VR-based interactive art installation that critically explores the exploitation and marginalization of data annotation workers, whose labor is fundamental to the advancement of artificial intelligence (AI) and machine learning (ML). These essential yet often invisible laborers function as the “resource mine” within the technological infrastructure, embodying the commodification and alienation of human labor as described by Marx and Heidegger’s concept of “enframing.” The installation aims to symbolically reconstruct the monotonous nature of data‑annotation tasks, invitin

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Nasim, Md, Xinghang Zhang, Anter El-Azab, and Yexiang Xue. "End-to-End Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23005–11. http://dx.doi.org/10.1609/aaai.v38i21.30342.

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The availability of tera-byte scale experiment data calls for AI driven approaches which automatically discover scientific models from data. Nonetheless, significant challenges present in AI-driven scientific discovery: (i) The annotation of large scale datasets requires fundamental re-thinking in developing scalable crowdsourcing tools. (ii) The learning of scientific models from data calls for innovations beyond black-box neural nets. (iii) Novel visualization & diagnosis tools are needed for the collaboration of experimental and theoretical physicists, and computer scientists. We presen

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Ponnusamy, Pragaash, Alireza Roshan Ghias, Chenlei Guo, and Ruhi Sarikaya. "Feedback-Based Self-Learning in Large-Scale Conversational AI Agents." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (2020): 13180–87. http://dx.doi.org/10.1609/aaai.v34i08.7022.

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Today, most of the large-scale conversational AI agents such as Alexa, Siri, or Google Assistant are built using manually annotated data to train the different components of the system including Automatic Speech Recognition (ASR), Natural Language Understanding (NLU) and Entity Resolution (ER). Typically, the accuracy of the machine learning models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly and

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Ponnusamy, Pragaash, Alireza Ghias, Yi Yi, Benjamin Yao, Chenlei Guo, and Ruhi Sarikaya. "Feedback-Based Self-Learning in Large-Scale Conversational AI Agents." AI Magazine 42, no. 4 (2022): 43–56. http://dx.doi.org/10.1609/aimag.v42i4.15102.

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Today, most of the large-scale conversational AI agents such as Alexa, Siri, or Google Assistant are built using manually annotated data to train the different components of the system including automatic speech recognition (ASR), natural language understanding (NLU), and entity resolution (ER). Typically, the accuracy of the machine learning models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly an

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Ponnusamy, Pragaash, Alireza Ghias, Yi Yi, Benjamin Yao, Chenlei Guo, and Ruhi Sarikaya. "Feedback-Based Self-Learning in Large-Scale Conversational AI Agents." AI Magazine 42, no. 4 (2022): 43–56. http://dx.doi.org/10.1609/aaai.12025.

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Today, most of the large-scale conversational AI agents such as Alexa, Siri, or Google Assistant are built using manually annotated data to train the different components of the system including automatic speech recognition (ASR), natural language understanding (NLU), and entity resolution (ER). Typically, the accuracy of the machine learning models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly an

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Ibodullayev, Sardor Nasriddin o'g'li, and Yaxyobek Sobirjon o'g'li Sodiqjonov. "ARTIFICIAL INTELLIGENCE AND ITS METHODS IN VIRTUAL REALITY." "Yosh mutaxassislar" ilmiy-amaliy jurnali. 2023-06, no. 1 (2023): 57–64. https://doi.org/10.5281/zenodo.8043452.

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<strong>Annotation</strong>: Artificial intelligence (AI) has the potential to revolutionize the way we experience virtual reality (VR). By using machine learning algorithms and other AI-powered techniques, developers can create VR experiences that are more immersive, interactive, and personalized. AI can be used to create intelligent avatars that can interact with users in real-time, recognize objects in VR environments, create more sophisticated chatbots, and detect and respond to user emotions in real-time. As AI technology continues to evolve, we can expect to see even more innovative and

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Radeta, Marko, Ruben Freitas, Claudio Rodrigues, et al. "Man and the Machine: Effects of AI-assisted Human Labeling on Interactive Annotation of Real-Time Video Streams." ACM Transactions on Interactive Intelligent Systems, February 29, 2024. http://dx.doi.org/10.1145/3649457.

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AI-assisted interactive annotation is a powerful way to facilitate data annotation – a prerequisite for constructing robust AI models. While AI-assisted interactive annotation has been extensively studied in static settings, less is known about its usage in dynamic scenarios where the annotators operate under time and cognitive constraints, e.g., while detecting suspicious or dangerous activities from real-time surveillance feeds. Understanding how AI can assist annotators in these tasks and facilitate consistent annotation is paramount to ensure high performance for AI models trained on these

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