22 citations · 36 across the 6 of their papers we have counts for
6 papers
Uncovering Memorization Effect in the Presence of Spurious Correlations
Chenyu You, Haocheng Dai, Yifei Min +3
Machine learning models often rely on simple spurious features -- patterns in training data that correlate with targets but are not causally related to them, like image backgrounds…
Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations
Chenyu You, Yifei Min, Weicheng Dai +3
Fine-tuning pre-trained vision-language models, like CLIP, has yielded success on diverse downstream tasks. However, several pain points persist for this paradigm: (i) directly tun…
Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
Chenyu You, Weicheng Dai, Yifei Min +2
Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation. Towards this end, recent deep lear…
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
Chenyu You, Weicheng Dai, Yifei Min +3
Medical data often exhibits long-tail distributions with heavy class imbalance, which naturally leads to difficulty in classifying the minority classes (i.e., boundary regions or r…
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels
Chenyu You, Weicheng Dai, Fenglin Liu +6
Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly fo…
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation
Chenyu You, Weicheng Dai, Yifei Min +2
Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class d…