Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning

Abstract

Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue. In addition, there may be interobserver variability that might lead to conflict in the final diagnosis. As a result, there is a crucial need of developing an intelligent automated method that can learn from the patterns themselves and assist the pathologist in making a faster, accurate, and consistent decision for determining the normal and abnormal region in the colorectal tissues. Moreover, the intelligent method should be able to localize the abnormal region in the whole slide image (WSI), which will make it easier for the pathologists to focus on only the region of interest making the task of tissue examination faster and lesser time-consuming. As a result, artificial intelligence (AI)-based classification and localization models are proposed for determining and localizing the abnormal regions in WSI. The proposed models achieved F-score of 0.97, area under curve (AUC) 0.97 with pretrained Inception-v3 model, and F-score of 0.99 and AUC 0.99 with customized Inception-ResNet-v2 Type 5 (IR-v2 Type 5) model.

Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer

“…Computer-aided diagnosis using deep learning methods showed good performance for classification, prognostication, and prediction of breast cancer, prostate cancer, gastrointestinal cancer, skin cancer, etc. [19][20][21][22][23][24].…”

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

“…Moreover, a large dataset for model training is also essential for improved prediction results. For example, Gupta et al [ 119 ] used the Inception-ResNet-v2 model to obtain superior performance (F-Score, 0.99; AUC, 0.99) in classifying and locating abnormal and normal tissue regions based on a model trained with more than 1,000,000 WSI patches. Despite the relatively low network complexity of the Inception-ResNet-v2 model (56 million parameters), network performance can be compensated with the support of a large amount of training data.…”

Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants

“…Moreover, DL based approaches are actively employed for pattern classification or analysis of the medical images. There are extensive studies conducted with numerous methods focusing on the individual diagnosis of colorectal cancer from histopathology images, such as classification of colorectal adenocarcinoma [ [2], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]], colon polyp classification [ [16], [17], [4], [18], [19], [20], [21]] and colon gland classification [ [5], [22], [23]].…”

“…Three experts evaluate performance of the suggested methodology. Similarly, Gupta et al [6] use a customized Inception-ResNet-v2 Type 5 (IR-v2 Type 5) model for classification and localization of the abnormal tissues from colonic WSIs. In their work, Ho et al [7] classify colonic biopsy WSIs as high risk and low risk with an AUC of 91.7%.…”