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Found 5 papers, 4 papers with code
Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations
As a piloting study, this work focuses on exploring mitigating the reliance on spurious features for CLIP without using any group annotation.
Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation.
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation.
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels
Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention.
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation
In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation.
