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Found 5 papers, 0 papers with code
Improved statistical benchmarking of digital pathology models using pairwise frames evaluation
Nested pairwise frames is a method for relative benchmarking of cell or tissue digital pathology models against manual pathologist annotations on a set of sampled patches.
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.
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.
