Semi-supervised learning for cancer detection of lymph node metastases

Published in arXiv preprint arXiv:1906.09587, 2019

Recommended citation: Amit Jaiswal, Ivan Panshin, Dimitrij Shulkin, Nagender Aneja, Samuel Abramov "Semi-supervised learning for cancer detection of lymph node metastases." arXiv preprint arXiv:1906.09587, 2019.

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Abstract: Pathologists find it tedious to examine the status of the sentinel lymph node on a large number of pathological scans. The examination process of such lymph nodes, which encompasses metastasized cancer cells, is histopathologically organized. However, finding metastatic tissues is gradual, which is often challenging. This work presents our deep convolutional neural network-based model validated on the PatchCamelyon (PCam) benchmark dataset for fundamental machine learning research in histopathology diagnosis. Our proposed model trained with a semi-supervised learning approach using pseudo labels on PCam-level significantly leads to better performances to a strong CNN baseline on the AUC metric.