Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases

TL;DR

This paper introduces a semi-supervised deep learning model for detecting cancer metastases in lymph nodes, demonstrating improved accuracy on a histopathology benchmark dataset.

Contribution

The work presents a novel semi-supervised training approach using pseudo labels for cancer detection in histopathology images, enhancing model performance over traditional CNNs.

Findings

Improved AUC performance on PCam dataset

Semi-supervised approach outperforms baseline CNNs

Effective use of pseudo labels in medical image analysis

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.