Weighted multi-level deep learning analysis and framework for processing breast cancer WSIs

TL;DR

This paper introduces a weighted multi-level deep learning framework for breast cancer whole slide image analysis, significantly improving classification accuracy by integrating information across multiple tissue levels.

Contribution

It proposes a novel multi-level deep learning approach that combines information from different tissue levels, enhancing diagnostic accuracy over existing methods.

Findings

Accuracy increased from 72.2% to 84.8%.

Global tissue architecture improves classification performance.

Multi-level weighting enhances diagnostic reliability.

Abstract

Prevention and early diagnosis of breast cancer (BC) is an essential prerequisite for the selection of proper treatment. The substantial pressure due to the increase of demand for faster and more precise diagnostic results drives for automatic solutions. In the past decade, deep learning techniques have demonstrated their power over several domains, and Computer-Aided (CAD) diagnostic became one of them. However, when it comes to the analysis of Whole Slide Images (WSI), most of the existing works compute predictions from levels independently. This is, however, in contrast to the histopathologist expert approach who requires to see a global architecture of tissue structures important in BC classification. We present a deep learning-based solution and framework for processing WSI based on a novel approach utilizing the advantages of image levels. We apply the weighing of information extracted from several levels into the final classification of the malignancy. Our results demonstrate the profitability of global information with an increase of accuracy from 72.2% to 84.8%.