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Found 6 papers, 0 papers with code
ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology
Secondly, dependency on domain labels prevents the use of pathology images without domain labels to improve model performance.
Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology
Machine learning algorithms have the potential to improve patient outcomes in digital pathology.
SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology
We leverage the idea that decoupling feature and classifier learning can lead to improved decision boundaries for label imbalanced datasets.
Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts
Histopathology images are gigapixel-sized and include features and information at different resolutions.
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology
Our Additive MIL models enable spatial credit assignment such that the contribution of each region in the image can be exactly computed and visualized.
Rethinking Machine Learning Model Evaluation in Pathology
Machine Learning has been applied to pathology images in research and clinical practice with promising outcomes.
