Big Self-Supervised Models Advance Medical Image Classification [PDF]
IEEE International Conference on Computer Vision, 2021 Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis.
Shekoofeh Azizi +11 more
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SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification [PDF]
Computer Vision and Pattern Recognition, 2021 A common classification task situation is where one has a large amount of data available for training, but only a small portion is annotated with class labels.
Zijian Hu +3 more
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Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised Classification [PDF]
International Joint Conference on Artificial Intelligence, 2020 Graph neural network (GNN) and label propagation algorithm (LPA) are both message passing algorithms, which have achieved superior performance in semi-supervised classification.
Yunsheng Shi +5 more
semanticscholar +1 more source
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases [PDF]
Computer Vision and Pattern Recognition, 2017 The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging studies accompanied by radiological reports are accumulated and stored in many modern
Xiaosong Wang +5 more
semanticscholar +1 more source
npj Digit. Medicine, 2023 Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes.
Shih-Cheng Huang +5 more
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Unveiling Patterns: A Study on Semi-Supervised Classification of Strip Surface Defects
IEEE Access, 2023 As a critical intermediate material in the iron and steel industry, strip steel will inevitably have various surface defects during its processing, which directly affects the service performance and life of the material.
Yongfei Liu, Haoyu Yang, Chenwei Wu
semanticscholar +1 more source
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning [PDF]
Computer Vision and Pattern Recognition, 2020 We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations.
Bin Li, Yin Li, K. Eliceiri
semanticscholar +1 more source
Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has shown great improvement in extracting useful representations from unlabeled ...
Emadeldeen Eldele +6 more
semanticscholar +1 more source
Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [PDF]
Jisuanji kexue, 2022 Consistency-based semi-supervised learning methods typically use simple data augmentation methods to achieve consistent predictions for both original inputs and perturbed inputs.The effectiveness of this approach is difficult to be guaranteed when the ...
XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo
doaj +1 more source
Supervised Classification Problems–Taxonomy of Dimensions and Notation for Problems Identification
IEEE Access, 2021 The paper proposes a taxonomy for categorizing the main features of the supervised learning classification problems and a notation for the identification of the supervised learning classification problem categories.
Ireneusz Czarnowski, Piotr Jedrzejowicz
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