Semi-supervised learning for cancer detection of lymph node metastases

Amit Kumar Jaiswal
,
Ivan Panshin
,
Dimitrij Shulkin
,
Nagender Aneja
,
Samuel Abramov

Research Output:

Contribution to conference

Paper

Peer-review

Open access

Sustainable Development Goals

SDG 3 Good Health and Well

Abstract

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