scholarly journals Deep Learning for Histopathological Image Analysis

2021 ◽  
pp. 153-169
Author(s):  
Cédric Wemmert ◽  
Jonathan Weber ◽  
Friedrich Feuerhake ◽  
Germain Forestier
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Histopathological Image ◽  
Histopathological Image Analysis

Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis

Methods ◽  
2020 ◽  
Vol 179 ◽  
pp. 3-13 ◽  
Author(s):  
Sai Chandra Kosaraju ◽  
Jie Hao ◽  
Hyun Min Koh ◽  
Mingon Kang
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Receptive Field ◽  
Multi Scale ◽  
Histopathological Image ◽  
Histopathological Image Analysis

scholarly journals Machine Learning Techniques used for the Histopathological Image Analysis of Oral Cancer-A Review

2020 ◽  
Vol 13 (1) ◽  
pp. 106-118
Author(s):  
Santisudha Panigrahi ◽  
Tripti Swarnkar
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Oral Cancer ◽  
Oral Disease ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Histopathological Image Analysis

Oral diseases are the 6th most revealed malignancy happening in head and neck regions found mainly in south Asian countries. It is the most common cancer with fourteen deaths in an hour on a yearly basis, as per the WHO oral cancer incidence in India. Due to the cost of tests, mistakes in the recognition procedure, and the enormous remaining task at hand of the cytopathologist, oral growths cannot be diagnosed promptly. This area is open to be looked into by biomedical analysts to identify it at an early stage. At present, with the advent of entire slide computerized scanners and tissue histopathology, there is a gigantic aggregation of advanced digital histopathological images, which has prompted the necessity for their analysis. A lot of computer aided analysis techniques have been developed by utilizing machine learning strategies for prediction and prognosis of cancer. In this review paper, first various steps of obtaining histopathological images, followed by the visualization and classification done by the doctors are discussed. As machine learning techniques are well known, in the second part of this review, the works done for histopathological image analysis as well as other oral datasets using these strategies for growth prognosis and anticipation are discussed. Comparing the pitfalls of machine learning and how it has overcome by deep learning mostly for image recognition tasks are also discussed subsequently. The third part of the manuscript describes how deep learning is beneficial and widely used in different cancer domains. Due to the remarkable growth of deep learning and wide applicability, it is best suited for the prognosis of oral disease. The aim of this review is to provide insight to the researchers opting to work for oral cancer by implementing deep learning and artificial neural networks.

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