Histopathologic Image Processing: A Review
This is an incremental review and case study aimed at pathologists and researchers to accelerate tumor analysis using computer-aided systems.
The paper reviews computing techniques for processing histopathologic images to aid diagnosis, and presents a case study where a hybrid deep and shallow learning method achieved 91% accuracy in breast cancer classification, outperforming the baseline.
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.