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ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification.

, , , , , and . CVPR, page 20665-20674. IEEE, (2022)

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Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation., , , , , and . CoRR, (2021)ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification., , , , , and . CVPR, page 20665-20674. IEEE, (2022)ACPL: Anti-curriculum Pseudo-labelling forSemi-supervised Medical Image Classification., , , , , and . CoRR, (2021)Translation Consistent Semi-supervised Segmentation for 3D Medical Images., , , , , , and . CoRR, (2022)Learning Support and Trivial Prototypes for Interpretable Image Classification., , , , , , , and . ICCV, page 2062-2072. IEEE, (2023)Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images., , , , , , , and . Medical Image Anal., (December 2023)Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning., , , , and . CoRR, (2023)Noisy Label Learning for Large-scale Medical Image Classification., , , , , and . CoRR, (2021)BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification., , , , , , and . ICCV, page 21227-21238. IEEE, (2023)Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models., , , , , , , , and . MICCAI (3), volume 13433 of Lecture Notes in Computer Science, page 14-24. Springer, (2022)