Journal ArticleDOI
Automatic mitosis detection in breast histopathology images using Convolutional Neural Network based deep transfer learning
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TLDR
Experiments show that the pre-trained Convolutional Neural Network model outperforms conventionally used detection systems and provides at least 15% improvement in F-score on other state-of-the-art techniques.About:
This article is published in Biocybernetics and Biomedical Engineering.The article was published on 2019-01-01. It has received 45 citations till now. The article focuses on the topics: Convolutional neural network & Deep belief network.read more
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Journal ArticleDOI
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis.
TL;DR: The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input or to improve the results of the network output (post-processing), focusing on digital pathology image analysis.
Journal ArticleDOI
A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
Xiaomin Zhou,Chen Li,Mamunur Rahaman,Yu-Dong Yao,Shiliang Ai,Changhao Sun,Qian Wang,Yong Zhang,Mo Li,Xiaoyan Li,Tao Jiang,Dan Xue,Shouliang Qi,Yueyang Teng +13 more
TL;DR: This review presents a comprehensive overview of the BHIA techniques based on ANNs, and categorizes the existing models into classical and deep neural networks for in-depth investigation.
Journal ArticleDOI
Automated classification of histopathology images using transfer learning.
TL;DR: In this article, a deep learning based transfer learning approach has been proposed to classify histopathology images automatically, which can improve the quality of diagnoses and give pathologists a second opinion for critical cases.
Journal ArticleDOI
Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening.
TL;DR: A novel hybrid transfer learning technique is presented, in which a CNN is built and trained from scratch, with initial weights of only those filters which were identified as relevant using AlexNet and VGG-16 net to detect cervical cancer from cervix images.
Journal ArticleDOI
MitosisNet: End-to-End Mitotic Cell Detection by Multi-Task Learning
TL;DR: An end-to-end multi-task learning system for mitosis detection from pathological images which is named “MitosisNet” and demonstrates state- of-the-art performance compared to the existing methods and the proposed approach is fast enough in order to meet the requirements of clinical practice.
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