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Found 4 papers, 1 papers with code
Investigating the Robustness of Vision Transformers against Label Noise in Medical Image Classification
Label noise in medical image classification datasets significantly hampers the training of supervised deep learning methods, undermining their generalizability.
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical Heterogeneity
Purpose: We apply federated learning to train an OCT image classifier simulating a realistic scenario with multiple clients and statistical heterogeneous data distribution where data in the clients lack samples of some categories entirely.
Cross-modal Contrastive Learning with Asymmetric Co-attention Network for Video Moment Retrieval
Video moment retrieval is a challenging task requiring fine-grained interactions between video and text modalities.
Medical Vision Language Pretraining: A survey
Medical Vision Language Pretraining (VLP) has recently emerged as a promising solution to the scarcity of labeled data in the medical domain.
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