Relevant bibliographies by topics / Inter-dataset variability

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Academic literature on the topic 'Inter-dataset variability'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Inter-dataset variability"

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Zhao, Kunkun, Zhisheng Zhang, Haiying Wen, and Alessandro Scano. "Intra-Subject and Inter-Subject Movement Variability Quantified with Muscle Synergies in Upper-Limb Reaching Movements." Biomimetics 6, no. 4 (2021): 63. http://dx.doi.org/10.3390/biomimetics6040063.

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Quantifying movement variability is a crucial aspect for clinical and laboratory investigations in several contexts. However, very few studies have assessed, in detail, the intra-subject variability across movements and the inter-subject variability. Muscle synergies are a valuable method that can be used to assess such variability. In this study, we assess, in detail, intra-subject and inter-subject variability in a scenario based on a comprehensive dataset, including multiple repetitions of multi-directional reaching movements. The results show that muscle synergies are a valuable tool for q
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Kanagasundaram, Ahilan. "Improving the performance of GPLDA speaker verification using unsupervised inter-dataset variability compensation approaches." International Journal of Speech Technology 21, no. 3 (2018): 533–44. http://dx.doi.org/10.1007/s10772-018-9513-x.

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Rai, Taranpreet, Ambra Morisi, Barbara Bacci, et al. "Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours." Cancers 16, no. 3 (2024): 644. http://dx.doi.org/10.3390/cancers16030644.

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Performing a mitosis count (MC) is the diagnostic task of histologically grading canine Soft Tissue Sarcoma (cSTS). However, mitosis count is subject to inter- and intra-observer variability. Deep learning models can offer a standardisation in the process of MC used to histologically grade canine Soft Tissue Sarcomas. Subsequently, the focus of this study was mitosis detection in canine Perivascular Wall Tumours (cPWTs). Generating mitosis annotations is a long and arduous process open to inter-observer variability. Therefore, by keeping pathologists in the loop, a two-step annotation process
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Setiawan, Fachruddin Ari, Dio Alif Pradana, and Iim Nandang. "Analysis of Inter-Subject and Session Variability using Brain Topographic Map." Journal of Applied Intelligent System 9, no. 1 (2025): 101–8. https://doi.org/10.62411/jais.v9i1.10051.

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The study described investigates the application of Brain-Computer Interface (BCI) technology, focusing on Motor Imagery (MI) signals which enable individuals to control movements through mental visualization. A major challenge in this field is accurately distinguishing between different movements, particularly when dealing with data from multiple subjects and recording sessions, known as inter-subject and inter-session variability. To address this, the authors employ the Wavelet Packet Transform-Common Spatial Patterns (WPT-CSP) method to enhance the resolution of MI signals. They visualize t
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Hannouche, A., G. Chebbo, G. Ruban, B. Tassin, B. J. Lemaire, and C. Joannis. "Relationship between turbidity and total suspended solids concentration within a combined sewer system." Water Science and Technology 64, no. 12 (2011): 2445–52. http://dx.doi.org/10.2166/wst.2011.779.

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This article confirms the existence of a strong linear relationship between turbidity and total suspended solids (TSS) concentration. However, the slope of this relation varies between dry and wet weather conditions, as well as between sites. The effect of this variability on estimating the instantaneous wet weather TSS concentration is assessed on the basis of the size of the calibration dataset used to establish the turbidity – TSS relationship. Results obtained indicate limited variability both between sites and during dry weather, along with a significant inter-event variability. Moreover,
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Fernández, Néstor, Jacinto Román, and Miguel Delibes. "Variability in primary productivity determines metapopulation dynamics." Proceedings of the Royal Society B: Biological Sciences 283, no. 1828 (2016): 20152998. http://dx.doi.org/10.1098/rspb.2015.2998.

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Temporal variability in primary productivity can change habitat quality for consumer species by affecting the energy levels available as food resources. However, it remains unclear how habitat-quality fluctuations may determine the dynamics of spatially structured populations, where the effects of habitat size, quality and isolation have been customarily assessed assuming static habitats. We present the first empirical evaluation on the effects of stochastic fluctuations in primary productivity—a major outcome of ecosystem functions—on the metapopulation dynamics of a primary consumer. A uniqu
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Yang, Shih-Hung, Yao-Mao Cheng, Jyun-We Huang, and Yon-Ping Chen. "RFaNet: Receptive Field-Aware Network with Finger Attention for Fingerspelling Recognition Using a Depth Sensor." Mathematics 9, no. 21 (2021): 2815. http://dx.doi.org/10.3390/math9212815.

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Automatic fingerspelling recognition tackles the communication barrier between deaf and hearing individuals. However, the accuracy of fingerspelling recognition is reduced by high intra-class variability and low inter-class variability. In the existing methods, regular convolutional kernels, which have limited receptive fields (RFs) and often cannot detect subtle discriminative details, are applied to learn features. In this study, we propose a receptive field-aware network with finger attention (RFaNet) that highlights the finger regions and builds inter-finger relations. To highlight the dis
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Hasson, S., V. Lucarini, and S. Pascale. "Hydrological cycle over South and Southeast Asian river basins as simulated by PCMDI/CMIP3 experiments." Earth System Dynamics 4, no. 2 (2013): 199–217. http://dx.doi.org/10.5194/esd-4-199-2013.

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Abstract. We investigate how the climate models contributing to the PCMDI/CMIP3 dataset describe the hydrological cycle over four major South and Southeast Asian river basins (Indus, Ganges, Brahmaputra and Mekong) for the 20th, 21st (13 models) and 22nd (10 models) centuries. For the 20th century, some models do not seem to conserve water at the river basin scale up to a good degree of approximation. The simulated precipitation minus evaporation (P − E), total runoff (R) and precipitation (P) quantities are neither consistent with the observations nor among the models themselves. Most of the
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Watson, S. J., P. Kritharas, and G. J. Hodgson. "Wind speed variability across the UK between 1957 and 2011." Wind Energy 18, no. 1 (2013): 21–42. http://dx.doi.org/10.1002/we.1679.

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ABSTRACTBy using surface data from 57 UK meteorological stations, a national [British Atmospheric Data Centre (BADC)‐57] and regional wind index for the UK has been calculated for the period 1983–2011. For a subset of seven stations, an additional national index (BADC‐7) has been calculated for the period 1957–2011. The indices show an annual variability of 4% over their respective periods, corresponding to a variation in typical wind turbine capacity factor of 7%. These indices are compared with indices calculated from other sources, namely an index generated using a gridded dataset of observ
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Cammarota, Camillo, and Mario Curione. "Trend Extraction in Functional Data of Amplitudes of R and T Waves in Exercise Electrocardiogram." Fluctuation and Noise Letters 16, no. 02 (2017): 1750014. http://dx.doi.org/10.1142/s0219477517500146.

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The amplitudes of R and T waves of the electrocardiogram (ECG) recorded during the exercise test show both large inter- and intra-individual variability in response to stress. We analyze a dataset of 65 normal subjects undergoing ambulatory test. We model the dataset of R and T series in the framework of functional data, assuming that the individual series are realizations of a non-stationary process, centered at the population trend. We test the time variability of this trend computing a simultaneous confidence band and the zero crossing of its derivative. The analysis shows that the amplitud
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Book chapters on the topic "Inter-dataset variability"

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Parmeggiani, Domenico, Marco De Sio, Giancarlo Moccia, et al. "Artificial Intelligence in Prostate Cancer Diagnosis: “Synergy-Net” in Campania FESR-POR (European Fund of Regional Development—Regional Operative Program) Research Project." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4581-4_23.

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AbstractBackground: The diagnosis of prostate cancer can only be obtained following the analysis of the tissue taken by means of a biopsy. Given the position of the organ, the biopsy is typically assisted by ultrasound images and the procedure consists of taking different portions of tissue from different areas, according to a map well-defined by international standards. Given the invasiveness of the procedure, the objective set within the Synergy-Net project is to analyze biomedical images in order to guide the operator on identifying the most suspicious tissues. Results: The dataset acquired
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Weishäupl, Kristina, Mario Schuster, Christina Mayrl, et al. "Impact of Professional Background on Inter-Annotator Variability and Accuracy During Annotation of Clinical Notes." In dHealth 2023. IOS Press, 2023. http://dx.doi.org/10.3233/shti230048.

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Background: The aging population’s need for treatment of chronic diseases is exhibiting a marked increase in urgency, with heart failure being one of the most severe diseases in this regard. To improve outpatient care of these patients and reduce hospitalization rates, the telemedical disease management program HerzMobil was developed in the past. Objective: This work aims to analyze the inter-annotator variability among two professional groups (healthcare and engineering) involved in this program’s annotation process of free-text clinical notes using categories. Methods: A dataset of 1,300 te
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Needham, R., R. Naemi, and N. Chockalingam. "Advancements in data analysis and visualisation techniques to support multiple single-subject analyses: an assessment of movement coordination and coordination variability." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210454.

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Vector coding is a data analysis technique that quantifies inter-segmental coordination and coordination variability of human movement. The usual reporting of vector coding time-series data can be difficult to interpret when multiple trials are superimposed on the same figure. This study describes and presents novel data visualisations for displaying data from vector coding that supports multiple single- subject analyses. The dataset used in this study describes the lumbar-pelvis coordination in the transverse plane during a gait cycle. The data visualisation techniques presented in this study
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Fatima, Kiran, and Hammad Majeed. "Texture-Based Evolutionary Method for Cancer Classification in Histopathology." In Medical Imaging. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0571-6.ch021.

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Real-world histology tissue textures owing to non-homogeneous nature and unorganized spatial intensity variations are complex to analyze and classify. The major challenge in solving pathological problems is inherent complexity due to high intra-class variability and low inter-class variation in texture of histology samples. The development of computational methods to assists pathologists in characterization of these tissue samples would have great diagnostic and prognostic value. In this chapter, an optimized texture-based evolutionary framework is proposed to provide assistance to pathologist
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Fatima, Kiran, and Hammad Majeed. "Texture-Based Evolutionary Method for Cancer Classification in Histopathology." In Advances in Data Mining and Database Management. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9767-6.ch004.

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Real-world histology tissue textures owing to non-homogeneous nature and unorganized spatial intensity variations are complex to analyze and classify. The major challenge in solving pathological problems is inherent complexity due to high intra-class variability and low inter-class variation in texture of histology samples. The development of computational methods to assists pathologists in characterization of these tissue samples would have great diagnostic and prognostic value. In this chapter, an optimized texture-based evolutionary framework is proposed to provide assistance to pathologist
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Forkert Nils Daniel, Suniaga Santiago, Fiehler Jens, Wersching Heike, Knecht Stefan, and Kemmling Andre. "Generation of a Probabilistic Arterial Cerebrovascular Atlas Derived from 700 Time-of-Flight MRA Datasets." In Studies in Health Technology and Informatics. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-101-4-148.

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The cerebral vasculature is a complex vessel network with high variations among human subjects. Although the coarse structure and spatial relationships of the main cerebrovascular branches are well known, not much knowledge about inter-individual vessel variability of humans at a finer level is available. The aim of this work is to present a probabilistic atlas of cerebral arterial vascular structures derived from 700 Time-of-Flight (TOF) magnetic resonance angiography (MRA) datasets of healthy subjects. Therefore, the cerebrovascular system was automatically segmented in each TOF datasets. In
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Palliyil Sreekumar, Sreelekshmi, Rohini Palanisamy, and Ramakrishnan Swaminathan. "Differentiation of Cell Painted Organelles Using Non Local Texture Descriptor and Random Forest Approach." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220626.

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Discriminating the cell organelles from microscopic images is a challenging task due to their high similarity in image appearance. In this work, an attempt has been made to differentiate nuclei, Endoplasmic Reticulum (ER) and cytoplasm using a texture pattern descriptor and Random Forest classifier. For this, Cell Painted public dataset from Broad Bioimage Benchmark collection are considered. Texture features are extracted from each image using Non Local Binary Pattern (NLBP) that captures the relationship between global pixels and sampling instances in a local neighborhood. Non local central
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Dash, Manoranjan, Raghu Indrakanti, and M. Narayana. "Effective Automated Medical Image Segmentation Using Hybrid Computational Intelligence Technique." In Blockchain and IoT based Smart Healthcare Systems. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815196290124010013.

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In biomedical domain, magnetic resonance imaging (MRI) segmentation is highly essential for the treatment or prevention of disease. The demand for fast processing and high accurate results is necessary for medical diagnosis. This can be solved by using computational intelligence (CoIn) for data processing. The CoIn can be achieved by using well-known techniques such as fuzzy logic, genetic algorithm, evolutionary algorithms and neural networks. The computational complexity of a medical image segmentation depends on the characteristics of data as well as suitable algorithms. The selection of Co
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Conference papers on the topic "Inter-dataset variability"

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Aronowitz, Hagai. "Inter dataset variability modeling for speaker recognition." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953188.

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Aronowitz, Hagai. "Inter dataset variability compensation for speaker recognition." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854353.

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Aronowitz, Hagai. "Compensating Inter-Dataset Variability in PLDA Hyper-Parameters for Robust Speaker Recognition." In The Speaker and Language Recognition Workshop (Odyssey 2014). ISCA, 2014. http://dx.doi.org/10.21437/odyssey.2014-42.

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Kanagasundaram, Ahilan, David Dean, and Sridha Sridharan. "Improving out-domain PLDA speaker verification using unsupervised inter-dataset variability compensation approach." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178853.

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Costa, Stephani S. H., Vagner Mendonça Gonçalves, Matheus A. O. Ribeiro, and Fátima L. S. Nunes. "Generalization of Cardiomyopathy Classification Models Based on Feature Descriptors from Magnetic Resonance Imaging." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2025. https://doi.org/10.5753/sbcas.2025.7899.

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Supervised Machine Learning (SML) models can help physicians to compose more accurate diagnoses. Due to the diversity of machines, systems, and protocols, exams conducted at different centers may have high variability, decreasing the generalization ability of these models. This paper aims to evaluate generalization ability of SML models for cardiomyopathy classification in Cardiac Magnetic Resonance Imaging (CMRI), using a set of left ventricle morphological and motion features. We performed cross-validation tests on two public CMRI databases, comparing intraand inter-dataset performances. The
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Costa, Stephani S. H., Vagner Mendonça Gonçalves, Matheus A. O. Ribeiro, and Fátima L. S. Nunes. "Investigating Cardiac Magnetic Resonance Imaging Feature Descriptors for Generalization of Cardiomyopathy Classification Models." In Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação (SBC), 2025. https://doi.org/10.5753/sbcas_estendido.2025.7909.

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Supervised Machine Learning (SML) models can help physicians to compose more accurate diagnoses. Due to the diversity of machines, systems, and protocols, exams conducted at different centers may have high variability, decreasing the model generalization ability. This paper aims to evaluate generalization ability of SML models for cardiomyopathy classification in Cardiac Magnetic Resonance Imaging (CMRI), using a set of left ventricle morphological and motion features. We performed cross-validation tests on two public CMRI databases, comparing intraand inter-dataset performances. The results a
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Ade-olusile, Binta, Zainb Dawod, and Saeed Sharif. "Early Detection of Arthritis Using Convolutional Neural Networks and Explainable AI." In Human Interaction and Emerging Technologies (IHIET-FS 2025): Future Systems and Artificial Intelligence Applications. AHFE International, 2025. https://doi.org/10.54941/ahfe1005960.

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Arthritis is a prevalent and debilitating musculoskeletal disorder that significantly impairs mobility, joint function, and overall quality of life for millions of individuals across the globe. The condition is characterized by chronic joint inflammation, cartilage degradation, stiffness, and persistent pain, often leading to long-term disability and increased healthcare dependency. As the global population continues to age, the healthcare impact and economic burden of arthritis are expected to rise substantially. Early detection and precise classification of arthritis are therefore essential
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Ade-olusile, Binta, Zainb Dawod, and Saeed Sharif. "Early Detection of Arthritis Using Convolutional Neural Networks and Explainable AI." In Human Interaction and Emerging Technologies (IHIET-FS 2025): Future Systems and Artificial Intelligence Applications. AHFE International, 2025. https://doi.org/10.54941/ahfe10059725960.

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Arthritis is a prevalent and debilitating musculoskeletal disorder that significantly impairs mobility, joint function, and overall quality of life for millions of individuals across the globe. The condition is characterized by chronic joint inflammation, cartilage degradation, stiffness, and persistent pain, often leading to long-term disability and increased healthcare dependency. As the global population continues to age, the healthcare impact and economic burden of arthritis are expected to rise substantially. Early detection and precise classification of arthritis are therefore essential
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Kunimaru, Takanori, Kunio Ota, Kenji Amano, and W. Russell Alexander. "Development of a Quality Management System (QMS) for Borehole Investigations: Part 2—Evaluation of Applicability of QMS Methodology for the Hydrochemical Dataset." In ASME 2010 13th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2010. http://dx.doi.org/10.1115/icem2010-40065.

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An appropriate QMS, which is among the first tools required for repository site characterisation, will save on effort by reducing errors and the requirement to resample and reanalyse–but this can only be guaranteed by continuously assessing if the system is truly fit-for-purpose and amending it as necessary based on the practical experience of the end-users on-site. A QA audit of hydrochemical datasets for boreholes HDB-1–11 from Horonobe URL project by JAEA has been carried out by the application of a formal QA analysis which is based on the methodology previously employed for groundwaters du
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