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Found 12 papers, 7 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.
Adaptive Correspondence Scoring for Unsupervised Medical Image Registration
As the unsupervised learning scheme relies on intensity constancy between images to establish correspondence for reconstruction, this introduces spurious error residuals that are not modeled by the typical training objective.
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.
Learning correspondences of cardiac motion from images using biomechanics-informed modeling
Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures.
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.
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain.
Class-Aware Adversarial Transformers for Medical Image Segmentation
In this work, we present CASTformer, a novel type of adversarial transformers, for 2D medical image segmentation.
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation
However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation.
Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation
In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions.
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required.
