Computational Pathology: Challenges and Promises for Tissue Analysis

TL;DR

This paper reviews current computational methods in pathology, highlighting challenges and future directions for integrating diverse biological data sources to improve cancer diagnosis and tissue analysis.

Contribution

It provides a comprehensive overview of existing computational pathology workflows and discusses future research challenges in this rapidly evolving field.

Findings

Assessment of heterogeneous data sources improves diagnostic accuracy

Automated analysis tools assist medical doctors in tissue evaluation

Future research directions include integrating multi-omics data

Abstract

The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.