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

Autor: Xiaoyan Li, Yong Zhang, Shouliang Qi, Yu-Dong Yao, Qian Wang, Mo Li, Tao Jiang, Yueyang Teng, Mamunur Rahaman, Dan Xue, Shiliang Ai, Xiaomin Zhou, Changhao Sun, Chen Li
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
medicine.medical_specialty
General Computer Science
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Machine learning
computer.software_genre
030218 nuclear medicine & medical imaging
Image (mathematics)
03 medical and health sciences
Breast cancer
0302 clinical medicine
convolutional neural networks
0202 electrical engineering
electronic engineering
information engineering
medicine
FOS: Electrical engineering
electronic engineering
information engineering
General Materials Science
Segmentation
Objectivity (science)
image segmentation
Artificial neural network
business.industry
Image and Video Processing (eess.IV)
General Engineering
deep learning
Electrical Engineering and Systems Science - Image and Video Processing
medicine.disease
Categorization
histopathology
Deep neural networks
020201 artificial intelligence & image processing
Histopathology
lcsh:Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
business
lcsh:TK1-9971
computer
image classification
Zdroj: IEEE Access, Vol 8, Pp 90931-90956 (2020)
DOI: 10.48550/arxiv.2003.12255
Popis: Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.
Comment: 25 pages,19 figures
Databáze: OpenAIRE
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