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Found 4 papers, 1 papers with code
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis
The tumor microenvironment (TME) plays a crucial role in cancer progression and treatment response, yet current methods for its comprehensive analysis in H&E-stained tissue slides face significant limitations in the diversity of tissue cell types and accuracy.
Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation
The domain shift in pathological segmentation is an important problem, where a network trained by a source domain (collected at a specific hospital) does not work well in the target domain (from different hospitals) due to the different image features.
Domain Adaptive Multiple Instance Learning for Instance-level Prediction of Pathological Images
We conducted experiments on the pathological image dataset we created for this study and showed that the proposed method significantly improves the classification performance compared to existing methods.
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification
Semi-supervised domain adaptation is a technique to build a classifier for a target domain by modifying a classifier in another (source) domain using many unlabeled samples and a small number of labeled samples from the target domain.
