Unsupervised Learning of Deep-Learned Features from Breast Cancer Images

TL;DR

This paper introduces an unsupervised machine learning method for detecting breast cancer in whole slide images, automating the process without human intervention and aiding in efficient diagnosis.

Contribution

It presents a fully automated unsupervised learning approach for breast cancer detection in whole slide images, including a prototype application for user interaction.

Findings

Effective detection of breast cancer in slide images

Automated clustering identifies relevant cancer regions

Prototype application supports user-guided analysis

Abstract

Detecting cancer manually in whole slide images requires significant time and effort on the laborious process. Recent advances in whole slide image analysis have stimulated the growth and development of machine learning-based approaches that improve the efficiency and effectiveness in the diagnosis of cancer diseases. In this paper, we propose an unsupervised learning approach for detecting cancer in breast invasive carcinoma (BRCA) whole slide images. The proposed method is fully automated and does not require human involvement during the unsupervised learning procedure. We demonstrate the effectiveness of the proposed approach for cancer detection in BRCA and show how the machine can choose the most appropriate clusters during the unsupervised learning procedure. Moreover, we present a prototype application that enables users to select relevant groups mapping all regions related to the groups in whole slide images.