TopOC: Topological Deep Learning for Ovarian and Breast Cancer Diagnosis

TL;DR

This paper introduces TopOC, a topological deep learning approach that combines topological data analysis with traditional methods to improve the accuracy and robustness of ovarian and breast cancer diagnosis from histopathological images.

Contribution

The paper presents a novel integration of topological data analysis with deep learning models to enhance cancer classification accuracy in histopathological images.

Findings

Topological features improve tumor type differentiation.

Enhanced model robustness with topological data integration.

Significant accuracy gains demonstrated on public datasets.

Abstract

Microscopic examination of slides prepared from tissue samples is the primary tool for detecting and classifying cancerous lesions, a process that is time-consuming and requires the expertise of experienced pathologists. Recent advances in deep learning methods hold significant potential to enhance medical diagnostics and treatment planning by improving accuracy, reproducibility, and speed, thereby reducing clinicians' workloads and turnaround times. However, the necessity for vast amounts of labeled data to train these models remains a major obstacle to the development of effective clinical decision support systems. In this paper, we propose the integration of topological deep learning methods to enhance the accuracy and robustness of existing histopathological image analysis models. Topological data analysis (TDA) offers a unique approach by extracting essential information through the evaluation of topological patterns across different color channels. While deep learning methods capture local information from images, TDA features provide complementary global features. Our experiments on publicly available histopathological datasets demonstrate that the inclusion of topological features significantly improves the differentiation of tumor types in ovarian and breast cancers.