Histopathologic Image Processing: A Review

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.

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

“…As far as we know, there exist some survey papers that summarize papers related to the BHIA work. (e.g., the reviews in [9], [12], [17]- [34]) In the following part, we go through the survey papers that are related to the BHIA work.…”

“…The survey of [9] reviews machine learning methods that are usually employed in histopathological image processing, such as segmentation, feature extraction, unsupervised learning, and supervised learning. More than 130 papers about histopathological image analysis are summarized, but only five are about BHIA with ANNs.…”

“…There are many techniques for breast cancer detection, such as X-ray mammography [4], 3-D Ultrasound (US) [5], Computed Tomography (CT), Positron Emission Tomography (PET) [6], Magnetic Resonance Imaging (MRI) [7], and breast temperature measurement [8]. However, pathological diagnosis is often regarded as a "golden standard" [9]. For better observation and analysis, the removed tissues usually need to be stained, where the Hematoxylin and Eosin (H&E) staining approach is the most common method.…”

GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification

“…The BC histological slides can be saved in the form of digital images by a digital image scanner. Many radiological techniques are existing such as: X-ray mammography, Computed Tomography (CT), ultra sound, functional Magnetic Resonance (fMRI) and other diagnosis techniques but histopathology considered as a golden mark for the diagnosis of several tumors [4], [5]. There several computer-aided diagnostic (CAD) systems are developed to overcome the cumbersome and time-consuming work of pathologists [6], [8].…”

“…Some of the labeled whole slide image (WSI) samples are depicted in Figure 1. Residual Nets and DenseNets are having their own class of architecture, one having the skip connection provision to solve the vanishing gradient problem, while the other has a dense network module with skip connections for deeper feature extraction [4], [5], [6], [7], [8], [9]. To overcome the training time for deep network, Transfer Learning (TL) is adopted by the researchers [7], [8], [9], [10], [11].…”