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Found 6 papers, 1 papers with code
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.
Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration
Transparency of Machine Learning models used for decision support in various industries becomes essential for ensuring their ethical use.
Non-readily identifiable data collaboration analysis for multiple datasets including personal information
This study then proposes a non-readily identifiable DC analysis only sharing non-readily identifiable data for multiple medical datasets including personal information.
Multi-Stage Pathological Image Classification using Semantic Segmentation
If we consider the relationship of neighboring patches and global features, we can improve the classification performance.
