Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
This work addresses a domain-specific problem for pathologists by improving cancer detection efficiency, but it appears incremental as it builds on existing methods with a semi-supervised approach.
The paper tackles the tedious task of detecting lymph node metastases in pathological scans by proposing a semi-supervised deep convolutional neural network model, which achieves better performance than a strong CNN baseline on the AUC metric when validated on the PatchCamelyon benchmark dataset.
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.