Cancer Metastasis Detection
1 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
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.
Domain adaptation strategies for cancer-independent detection of lymph node metastases
Furthermore, we show the effectiveness of repeated adaptation of networks from one cancer type to another to obtain multi-task metastasis detection networks.
