Computational Pathology: Challenges and Promises for Tissue Analysis
It addresses the challenge of integrating diverse data sources for pathology diagnosis, which is crucial for medical doctors, but is incremental as it focuses on reviewing existing methods.
The paper reviews the state-of-the-art in computational pathology workflows for analyzing heterogeneous medical data to aid in cancer detection and treatment, highlighting current designs and effectiveness while outlining future research directions.
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.