The Diagnostic Classification of the Pathological Image Using Computer Vision

被引:0
|
作者
Matsuzaka, Yasunari [1 ,2 ,3 ]
Yashiro, Ryu [3 ,4 ]
机构
[1] Showa Univ, Dept Microbiol & Immunol, Sch Med, Tokyo 1428555, Japan
[2] Univ Tokyo, Inst Med Sci, Ctr Gene & Cell Therapy, Div Mol & Med Genet, Tokyo 1088639, Japan
[3] Natl Inst Neurosci, Natl Ctr Neurol & Psychiat, Adm Sect Radiat Protect, Tokyo 1878551, Japan
[4] Natl Inst Infect Dis, Leprosy Res Ctr, Dept Mycobacteriol, Tokyo 1628640, Japan
关键词
computer vision; deep learning; convolutional neural networks; medical imaging data; ARTIFICIAL-INTELLIGENCE; CORONARY ATHEROSCLEROSIS; ANGIOGRAPHY; FLOW; AI;
D O I
10.3390/a18020096
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Computer vision and artificial intelligence have revolutionized the field of pathological image analysis, enabling faster and more accurate diagnostic classification. Deep learning architectures like convolutional neural networks (CNNs), have shown superior performance in tasks such as image classification, segmentation, and object detection in pathology. Computer vision has significantly improved the accuracy of disease diagnosis in healthcare. By leveraging advanced algorithms and machine learning techniques, computer vision systems can analyze medical images with high precision, often matching or even surpassing human expert performance. In pathology, deep learning models have been trained on large datasets of annotated pathology images to perform tasks such as cancer diagnosis, grading, and prognostication. While deep learning approaches show great promise in diagnostic classification, challenges remain, including issues related to model interpretability, reliability, and generalization across diverse patient populations and imaging settings.
引用
收藏
页数:32
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