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Found 2 papers, 1 papers with code
A Good Feature Extractor Is All You Need for Weakly Supervised Pathology Slide Classification
We question this belief in the context of weakly supervised whole slide image classification, motivated by the emergence of powerful feature extractors trained using self-supervised learning on diverse pathology datasets.
Deep Multiple Instance Learning with Distance-Aware Self-Attention
We evaluate our model on a custom MNIST-based MIL dataset that requires the consideration of relative spatial information, as well as on CAMELYON16, a publicly available cancer metastasis detection dataset, where we achieve a test AUROC score of 0. 91.
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