In medical Vision-Language Pre-training (VLP), significant work focuses on extracting text and image features from clinical reports and medical images. Yet, existing methods may overlooked the potential of the natural hierarchical structure in clinic...
Multiple instance learning (MIL) based whole slide image (WSI) classification is often carried out on the representations of patches extracted from WSI with a pre-trained patch encoder. The performance of classification relies on both patch-level rep...
Histological image registration is a fundamental task in histological image analysis. It is challenging because of substantial appearance differences due to multiple staining. Keypoint correspondences, i.e., matched keypoint pairs, have been introduc...
Deep learning models for medical image analysis easily suffer from distribution shifts caused by dataset artifact bias, camera variations, differences in the imaging station, etc., leading to unreliable diagnoses in real-world clinical settings. Doma...
Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated wit...
The recent advent of in-context learning (ICL) capabilities in large pre-trained models has yielded significant advancements in the generalization of segmentation models. By supplying domain-specific image-mask pairs, the ICL model can be effectively...
Self-supervised learning aims to learn transferable representations from unlabeled data for downstream tasks. Inspired by masked language modeling in natural language processing, masked image modeling (MIM) has achieved certain success in the field o...
Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data, especially on volumetric medical image segmentation. Unlike previous SSL methods which focus on exploring highly confident pseudo-labels or de...
In computational pathology, graphs have shown to be promising for pathology image analysis. There exist various graph structures that can discover differing features of pathology images. However, the combination and interaction between differing grap...
The interconnection between brain regions in neurological disease encodes vital information for the advancement of biomarkers and diagnostics. Although graph convolutional networks are widely applied for discovering brain connection patterns that poi...
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