Relevant bibliographies by topics / Deep learning segmentation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deep learning segmentation'

Author: Grafiati
Published: 19 April 2023
Last updated: 25 July 2025

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Journal articles on the topic "Deep learning segmentation"

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Dadras, Armin A., and Philipp Aichinger. "Deep Learning-Based Detection of Glottis Segmentation Failures." Bioengineering 11, no. 5 (2024): 443. http://dx.doi.org/10.3390/bioengineering11050443.

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Medical image segmentation is crucial for clinical applications, but challenges persist due to noise and variability. In particular, accurate glottis segmentation from high-speed videos is vital for voice research and diagnostics. Manual searching for failed segmentations is labor-intensive, prompting interest in automated methods. This paper proposes the first deep learning approach for detecting faulty glottis segmentations. For this purpose, faulty segmentations are generated by applying both a poorly performing neural network and perturbation procedures to three public datasets. Heavy data
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Ahmed Ali, Salim, and B. G Prasad. "Semantic Segmentation using Deep Learning Approaches - A Study." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 113–16. https://doi.org/10.21275/sr21630194436.

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Noori, Amani Y., Dr Shaimaa H. Shaker, and Dr Raghad Abdulaali Azeez. "Semantic Segmentation of Urban Street Scenes Using Deep Learning." Webology 19, no. 1 (2022): 2294–306. http://dx.doi.org/10.14704/web/v19i1/web19156.

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Scene classification is essential conception task used by robotics for understanding the environmental. The outdoor scene like urban street scene is composing of image with depth having greater variety than iconic object image. The semantic segmentation is an important task for autonomous driving and mobile robotics applications because it introduces enormous information need for safe navigation and complex reasoning. This paper introduces a model for classification all pixel’s image and predicates the right object that contains this pixel. This model adapts famous network image classification
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Weishaupt, L. L., T. Vuong, A. Thibodeau-Antonacci, et al. "A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING." Journal of the Canadian Association of Gastroenterology 5, Supplement_1 (2022): 140–42. http://dx.doi.org/10.1093/jcag/gwab049.120.

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Abstract Background Tumor delineation in endoscopy images is a crucial part of clinical diagnoses and treatment planning for rectal cancer patients. However, it is challenging to detect and adequately determine the size of tumors in these images, especially for inexperienced clinicians. This motivates the need for a standardized, automated segmentation method. While deep learning has proven to be a powerful tool for medical image segmentation, it requires a large quantity of high-quality annotated training data. Since the annotation of endoscopy images is prone to high inter-observer variabili
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AL-Oudat, Mohammad, Mohammad Azzeh, Hazem Qattous, Ahmad Altamimi, and Saleh Alomari. "Image Segmentation based Deep Learning for Biliary Tree Diagnosis." Webology 19, no. 1 (2022): 1834–49. http://dx.doi.org/10.14704/web/v19i1/web19123.

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Dilation of biliary tree can be an indicator of several diseases such as stones, tumors, benign strictures, and some cases cancer. This dilation can be due to many reasons such as gallstones, inflammation of the bile ducts, trauma, injury, severe liver damage. Automatic measurement of the biliary tree in magnetic resonance images (MRI) is helpful to assist hepatobiliary surgeons for minimally invasive surgery. In this paper, we proposed a model to segment biliary tree MRI images using a Fully Convolutional Neural (FCN) network. Based on the extracted area, seven features that include Entropy,
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Mrinalini, kakroo, and mansotra Vibhakar. "Automatic Segmentation of liver Tumor using Deep Learning." Journal of Scientific Research and Technology (JSRT) 1, no. 5 (2023): 100–113. https://doi.org/10.5281/zenodo.8276825.

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When it comes to medical imaging data like CT or MRI images, automatic segmentation of liver tumors is the process of precisely locating and isolating tumor spots without the need for human involvement. Liver tumor segmentation is crucial for accurate diagnosis and therapeutic planning of liver cancer. The purpose of this work is to provide a comprehensive summary of the state-of-the-art approaches to automatically segmenting liver cancers from medical imaging data. Here, we'll go through some of the more general and specialized approaches now in use in this field. By allowing for more pre
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Ng, Hwee Ping, Qijian Chan, Zheng Jie Tan, Ronnie Ssebaggala, and Joseph John Lifton. "Segmenting spatter particles on additively manufactured surfaces using deep learning." Surface Topography: Metrology and Properties 13, no. 1 (2025): 015006. https://doi.org/10.1088/2051-672x/ada6e1.

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Abstract Metal additively manufactured (AM) surfaces do not exhibit the same surface features as machined surfaces. Rather than cutting marks, the additive surface may display surface features such as spatter particles, weld tracks, cracks, and surface breaking pores. These features are not well described by surface height parameters that were developed for machined surfaces. Therefore, an AM specific surface characterisation approach is required; feature based surface characterisation is a promising approach, but it requires surface features to be manually segmented which is a subjective proc
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Sri, S. Vinitha, and S. P. Kavya. "Lung Segmentation Using Deep Learning." Asian Journal of Applied Science and Technology 05, no. 02 (2021): 10–19. http://dx.doi.org/10.38177/ajast.2021.5202.

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Vogt, Nina. "Neuron segmentation with deep learning." Nature Methods 16, no. 6 (2019): 460. http://dx.doi.org/10.1038/s41592-019-0450-7.

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Al-Behadili, Husam, Omar A. Athab, and Saddam K. Alwane. "Deep Learning Based Teeth Segmentation." Revue d'Intelligence Artificielle 38, no. 4 (2024): 1173–81. http://dx.doi.org/10.18280/ria.380411.

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Dissertations / Theses on the topic "Deep learning segmentation"

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Chen, Yifu. "Deep learning for visual semantic segmentation." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS200.

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Dans cette thèse, nous nous intéressons à la segmentation sémantique visuelle, une des tâches de haut niveau qui ouvre la voie à une compréhension complète des scènes. Plus précisément, elle requiert une compréhension sémantique au niveau du pixel. Avec le succès de l’apprentissage approfondi de ces dernières années, les problèmes de segmentation sémantique sont abordés en utilisant des architectures profondes. Dans la première partie, nous nous concentrons sur la construction d’une fonction de coût plus appropriée pour la segmentation sémantique. En particulier, nous définissons une nouvelle
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Favia, Federico. "Real-time hand segmentation using deep learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292930.

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Hand segmentation is a fundamental part of many computer vision systems aimed at gesture recognition or hand tracking. In particular, augmented reality solutions need a very accurate gesture analysis system in order to satisfy the end consumers in an appropriate manner. Therefore the hand segmentation step is critical. Segmentation is a well-known problem in image processing, being the process to divide a digital image into multiple regions with pixels of similar qualities. Classify what pixels belong to the hand and which ones belong to the background need to be performed within a real-time p
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Sarpangala, Kishan. "Semantic Segmentation Using Deep Learning Neural Architectures." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304.

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Wen, Shuangyue. "Automatic Tongue Contour Segmentation using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38343.

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Ultrasound is one of the primary technologies used for clinical purposes. Ultrasound systems have favorable real-time capabilities, are fast and relatively inexpensive, portable and non-invasive. Recent interest in using ultrasound imaging for tongue motion has various applications in linguistic study, speech therapy as well as in foreign language education, where visual-feedback of tongue motion complements conventional audio feedback. Ultrasound images are known to be difficult to recognize. The anatomical structure in them, the rapidity of tongue movements, also missing segments in some f
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¿, Ananya. "DEEP LEARNING METHODS FOR CROP AND WEED SEGMENTATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1528372119706623.

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Tosteberg, Patrik. "Semantic Segmentation of Point Clouds Using Deep Learning." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136793.

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In computer vision, it has in recent years become more popular to use point clouds to represent 3D data. To understand what a point cloud contains, methods like semantic segmentation can be used. Semantic segmentation is the problem of segmenting images or point clouds and understanding what the different segments are. An application for semantic segmentation of point clouds are e.g. autonomous driving, where the car needs information about objects in its surrounding. Our approach to the problem, is to project the point clouds into 2D virtual images using the Katz projection. Then we use pre-t
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Kolhatkar, Dhanvin. "Real-Time Instance and Semantic Segmentation Using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40616.

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In this thesis, we explore the use of Convolutional Neural Networks for semantic and instance segmentation, with a focus on studying the application of existing methods with cheaper neural networks. We modify a fast object detection architecture for the instance segmentation task, and study the concepts behind these modifications both in the simpler context of semantic segmentation and the more difficult context of instance segmentation. Various instance segmentation branch architectures are implemented in parallel with a box prediction branch, using its results to crop each instance's feature
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Wang, Wei. "Image Segmentation Using Deep Learning Regulated by Shape Context." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-227261.

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In recent years, image segmentation by using deep neural networks has made great progress. However, reaching a good result by training with a small amount of data remains to be a challenge. To find a good way to improve the accuracy of segmentation with limited datasets, we implemented a new automatic chest radiographs segmentation experiment based on preliminary works by Chunliang using deep learning neural network combined with shape context information. When the process was conducted, the datasets were put into origin U-net at first. After the preliminary process, the segmented images were
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Chen, Yani. "Deep Learning based 3D Image Segmentation Methods and Applications." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1547066297047003.

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Liu, Dongnan. "Supervised and Unsupervised Deep Learning-based Biomedical Image Segmentation." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24744.

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Biomedical image analysis plays a crucial role in the development of healthcare, with a wide scope of applications including the disease diagnosis, clinical treatment, and future prognosis. Among various biomedical image analysis techniques, segmentation is an essential step, which aims at assigning each pixel with labels of interest on the category and instance. At the early stage, the segmentation results were obtained via manual annotation, which is time-consuming and error-prone. Over the past few decades, hand-craft feature based methods have been proposed to segment the biomedical images
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Books on the topic "Deep learning segmentation"

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Wang, Xiaogang. Deep Learning in Object Recognition, Detection, and Segmentation. Now Publishers, 2016.

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Brain Tumor MRI Image Segmentation Using Deep Learning Techniques. Elsevier, 2022. http://dx.doi.org/10.1016/c2021-0-00056-0.

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Chaki, Jyotismita. Brain Tumor MRI Image Segmentation Using Deep Learning Techniques. Elsevier Science & Technology, 2021.

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Chaki, Jyotismita. Brain Tumor MRI Image Segmentation Using Deep Learning Techniques. Elsevier Science & Technology Books, 2021.

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Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, Deep RL, Unsupervised Learning, Object Detection and Segmentation, and More. de Gruyter GmbH, Walter, 2020.

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Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, Deep RL, Unsupervised Learning, Object Detection and Segmentation, and More, 2nd Edition. Packt Publishing, Limited, 2020.

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Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the i
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Book chapters on the topic "Deep learning segmentation"

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Hatamizadeh, Ali, Assaf Hoogi, Debleena Sengupta, et al. "Deep Active Lesion Segmentation." In Machine Learning in Medical Imaging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_12.

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Benoit, Alexandre, Badih Ghattas, Emna Amri, Joris Fournel, and Patrick Lambert. "Deep Learning for Semantic Segmentation." In Multi-faceted Deep Learning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74478-6_3.

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Moskalenko, Viktor, Nikolai Zolotykh, and Grigory Osipov. "Deep Learning for ECG Segmentation." In Studies in Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30425-6_29.

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Yang, Hao-Yu. "Deep Learning in Brain Segmentation." In Handbook of Artificial Intelligence in Biomedical Engineering. Apple Academic Press, 2020. http://dx.doi.org/10.1201/9781003045564-12.

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Kaur, Prabhjot, and Anand Muni Mishra. "Segmentation of Deep Learning Models." In Machine Learning for Edge Computing. CRC Press, 2022. http://dx.doi.org/10.1201/9781003143468-8.

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Liu, Han, Dewei Hu, Hao Li, and Ipek Oguz. "Medical Image Segmentation Using Deep Learning." In Machine Learning for Brain Disorders. Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_13.

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AbstractImage segmentation plays an essential role in medical image analysis as it provides automated delineation of specific anatomical structures of interest and further enables many downstream tasks such as shape analysis and volume measurement. In particular, the rapid development of deep learning techniques in recent years has had a substantial impact in boosting the performance of segmentation algorithms by efficiently leveraging large amounts of labeled data to optimize complex models (supervised learning). However, the difficulty of obtaining manual labels for training can be a major o
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Keydana, Sigrid. "Image Segmentation." In Deep Learning and Scientific Computing with R torch. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003275923-19.

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Ezeobiejesi, Jude, and Bir Bhanu. "Latent Fingerprint Image Segmentation Using Deep Neural Network." In Deep Learning for Biometrics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61657-5_4.

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Jalilian, Ehsaneddin, and Andreas Uhl. "Iris Segmentation Using Fully Convolutional Encoder–Decoder Networks." In Deep Learning for Biometrics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61657-5_6.

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Rashed, Hazem, Senthil Yogamani, Ahmad El-Sallab, Mohamed Elhelw, and Mahmoud Hassaballah. "Deep Semantic Segmentation in Autonomous Driving." In Deep Learning in Computer Vision. CRC Press, 2020. http://dx.doi.org/10.1201/9781351003827-6.

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Conference papers on the topic "Deep learning segmentation"

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Nash, Will, Tom Drummond, and Nick Birbilis. "Deep Learning AI for Corrosion Detection." In CORROSION 2019. NACE International, 2019. https://doi.org/10.5006/c2019-13267.

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Abstract Visual inspection is a vital component of asset management that stands to benefit from automation. Using artificial intelligence to assist inspections can increase safety, reduce access costs, provide objective classification, and integrate with digital asset management systems. The work presented herein investigates the impact of dataset size on Deep Learning for automatic detection of corrosion on steel assets. Dataset creation is typically one of the first steps when applying Machine Learning methods to a new task; and the real-world performance of models hinges on the quality and
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Mohamed, Ismail E., Nancy M. Salem, Ibrahim Sadek, and Lamees N. Mahmoud. "Focal Adhesions Segmentation Using Deep Learning." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10928059.

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Adhikary, Chandril, Surya Pratap Singh, Monalisa Dey, Soubik Ghosh, and Spandan Sahu. "Satellite Image Segmentation using Deep Learning." In 2025 8th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech). IEEE, 2025. https://doi.org/10.1109/iementech65115.2025.10959364.

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Erciyes, Kerem, Mustafa Soydan, and Ozgur Gumus. "Deep-Learning Based 3D Lung Segmentation." In 2025 13th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2025. https://doi.org/10.1109/isdfs65363.2025.11011979.

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Al Yusuf, Husain, Jayant Biradar, and Eung-Joo Lee. "Lightweight TransUNet with knowledge distillation for efficient medical image segmentation." In Real-Time Image Processing and Deep Learning 2025, edited by Nasser Kehtarnavaz and Mukul V. Shirvaikar. SPIE, 2025. https://doi.org/10.1117/12.3054578.

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Agrawal, Krishna K., and Gautam Kumar. "Canine Vertebral Column Segmentation Using Deep Learning." In 2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT). IEEE, 2024. http://dx.doi.org/10.1109/ic2sdt62152.2024.10696354.

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JiaHong, Li, Zhuo WeiHao, Zhao YanPing, and Liu WenJian. "Deep Learning-Based Tongue Image Segmentation Research." In 2024 2nd International Conference on Big Data and Privacy Computing (BDPC). IEEE, 2024. http://dx.doi.org/10.1109/bdpc59998.2024.10649129.

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Wang, Siyi, Xinyi Wen, Ying Wang, et al. "Deep Learning Based Oracle Segmentation and Recognition." In 2024 4th International Conference on Electronic Information Engineering and Computer Science (EIECS). IEEE, 2024. https://doi.org/10.1109/eiecs63941.2024.10799961.

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Tamang, Sudarshan, Awika Ariyametkul, May Phu Paing, and Toan H. Bui. "Deep Learning for Segmentation of Brain Tumors." In 2024 16th Biomedical Engineering International Conference (BMEiCON). IEEE, 2024. https://doi.org/10.1109/bmeicon64021.2024.10896265.

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Garcia-Salgado, Beatriz P., Nasser Kehtarnavaz, Volodymyr I. Ponomaryov, Rogelio Reyes-Reyes, and Jose A. Almaraz-Damian. "Efficient stroke lesion segmentation in MRI using a modified deep learning model." In Real-Time Image Processing and Deep Learning 2025, edited by Nasser Kehtarnavaz and Mukul V. Shirvaikar. SPIE, 2025. https://doi.org/10.1117/12.3053832.

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Reports on the topic "Deep learning segmentation"

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Panta, Manisha, Padam Thapa, Md Hoque, et al. Application of deep learning for segmenting seepages in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49453.

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Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. Therefore, it is essential always to be vigilant with regular monitoring procedures to identify seepages throughout these levee systems and perform adequate repairs to limit potential threats from unforeseen levee failures. This paper introduces a fully convolutional neural network to identify and segment seepage from the image in levee systems. To th
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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNe
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Alhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic database
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Chang, Ke-Vin. Deep Learning Algorithm for Automatic Localization and Segmentation of the Median Nerve: a Protocol for Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.5.0074.

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Review question / Objective: To explore/summarize the performance of deep learning in automatic localization and segmentation of the median nerve at the carpal tunnel level. Condition being studied: Participants with and without carpal tunnel syndrome. Information sources: The following electronic databases will be searched, encompassing PubMed, Medline, Embase and Web of Science. We target the studies investigating in the utility of deep neural network on the evaluation of the median nerve in the carpal tunnel.
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Huang, Haohang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John Hart. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2. Illinois Center for Transportation, 2022. http://dx.doi.org/10.36501/0197-9191/22-017.

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Riprap rock and aggregates are extensively used in structural, transportation, geotechnical, and hydraulic engineering applications. Field determination of morphological properties of aggregates such as size and shape can greatly facilitate the quality assurance/quality control (QA/QC) process for proper aggregate material selection and engineering use. Many aggregate imaging approaches have been developed to characterize the size and morphology of individual aggregates by computer vision. However, 3D field characterization of aggregate particle morphology is challenging both during the quarry
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Wang, Ting-Wei, Yun-Hsuan Tzeng, Jia-Sheng Hong, et al. Deep Learning Approaches for Aorta Segmentation in Computed Tomography Imaging: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.3.0121.

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Wang, Ting-Wei, Yun-Hsuan Tzeng, Jia-Sheng Hong, et al. The Role of Deep Learning in Aortic Aneurysm Segmentation and Detection from CT Scans: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.3.0126.

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Klobucar, Blaz. Urban Tree Detection in Historical Aerial Imagery of Sweden : a test in automated detection with open source Deep Learning models. Faculty of Landscape Architecture, Horticulture and Crop Production Science, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.7kn4q7vikr.

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Urban trees are a key component of the urban environment. In Sweden, ambitious goals have been expressed by authorities regarding the retention and increase of urban tree cover, aiming to mitigate climate change and provide a healthy, livable urban environment in a highly contested space. Tracking urban tree cover through remote sensing serves as an indicator of how past urban planning has succeeded in retaining trees as part of the urban fabric, and historical imagery spanning back decades for such analysis is widely available. This short study examines the viability of automated detection us
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Oostrom, Marjolein, Rogene Eichler West, Moses Obiri, et al. Data-driven Mapping of the Mouse Connectome: The utility of transfer learning to improve the performance of deep learning models performing axon segmentation on light-sheet microscopy images. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1985702.

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Wang, Ting-Wei, Yun-Hsuan Tzeng, Jia-Sheng Hong, et al. Systematic Review and Meta-Analysis of Aortic Dissection Diagnosis via CT: Evaluating Deep Learning for Detection Against Expert Analysis and Its Application in Detection and Segmentation. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.3.0125.

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