A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images

TL;DR

This paper introduces a semi-supervised deep learning framework using expectation maximization and active learning to improve pixel-wise breast cancer grading in histological images, significantly reducing annotation effort.

Contribution

It presents a novel semi-supervised EM-based framework that leverages unannotated data and active learning to enhance breast cancer grading accuracy.

Findings

Outperforms state-of-the-art methods in pixel-wise prediction.

Reduces annotation cost by over 70%.

Effectively utilizes unannotated datasets through collaborative filtering.

Abstract

Throughout the world, breast cancer is one of the leading causes of female death. Recently, deep learning methods are developed to automatically grade breast cancer of histological slides. However, the performance of existing deep learning models is limited due to the lack of large annotated biomedical datasets. One promising way to relieve the annotating burden is to leverage the unannotated datasets to enhance the trained model. In this paper, we first apply active learning method in breast cancer grading, and propose a semi-supervised framework based on expectation maximization (EM) model. The proposed EM approach is based on the collaborative filtering among the annotated and unannotated datasets. The collaborative filtering method effectively extracts useful and credible datasets from the unannotated images. Results of pixel-wise prediction of whole-slide images (WSI) demonstrate that the proposed method not only outperforms state-of-art methods, but also significantly reduces the annotation cost by over 70%.