Unsupervised Learning of Deep-Learned Features from Breast Cancer Images
This work addresses the labor-intensive process of manual cancer detection in medical imaging for pathologists, but it appears incremental as it builds on existing machine learning approaches for whole slide image analysis.
The paper tackles the problem of detecting cancer in breast invasive carcinoma whole slide images by proposing an unsupervised learning approach, resulting in a fully automated method that demonstrates effectiveness in cancer detection without human involvement.
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.