Histopathologic Image Processing: A Review

TL;DR

This review paper discusses computational techniques, including shallow and deep learning, for processing histopathologic images, highlighting challenges, datasets, and a case study on breast cancer classification with high accuracy.

Contribution

It provides a comprehensive overview of methods for histopathologic image analysis and presents a case study demonstrating improved classification accuracy.

Findings

Deep and shallow methods can effectively process HI.

The breast cancer classification achieved 91% accuracy.

The review highlights datasets and challenges in HI analysis.

Abstract

Histopathologic Images (HI) are the gold standard for evaluation of some tumors. However, the analysis of such images is challenging even for experienced pathologists, resulting in problems of inter and intra observer. Besides that, the analysis is time and resource consuming. One of the ways to accelerate such an analysis is by using Computer Aided Diagnosis systems. In this work we present a literature review about the computing techniques to process HI, including shallow and deep methods. We cover the most common tasks for processing HI such as segmentation, feature extraction, unsupervised learning and supervised learning. A dataset section show some datasets found during the literature review. We also bring a study case of breast cancer classification using a mix of deep and shallow machine learning methods. The proposed method obtained an accuracy of 91% in the best case, outperforming the compared baseline of the dataset.