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
相关论文
共 50 条
  • [31] Weed density classification in rice crop using computer vision
    Ashraf, Taskeen
    Khan, Yasir Niaz
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 175
  • [32] Automatic Classification of Chickpea Varieties Using Computer Vision Techniques
    Pourdarbani, Razieh
    Sabzi, Sajad
    Manuel Garcia-Amicis, Victor
    Garcia-Mateos, Gines
    Miguel Molina-Martinez, Jose
    Ruiz-Canales, Antonio
    AGRONOMY-BASEL, 2019, 9 (11):
  • [33] Fruit classification using computer vision and feedforward neural network
    Zhang, Yudong
    Wang, Shuihua
    Ji, Genlin
    Phillips, Preetha
    JOURNAL OF FOOD ENGINEERING, 2014, 143 : 167 - 177
  • [34] Performance Enhancement of Skin Cancer Classification Using Computer Vision
    Magdy, Ahmed
    Hussein, Hadeer
    Abdel-Kader, Rehab F.
    Abd El Salam, Khaled
    IEEE ACCESS, 2023, 11 : 72120 - 72133
  • [35] Shape extraction and classification of pizza base using computer vision
    Du, CJ
    Sun, DW
    JOURNAL OF FOOD ENGINEERING, 2004, 64 (04) : 489 - 496
  • [36] A Classification Module for Automated Mosquito Surveillance Using Computer Vision
    Fuchida, Masataka
    Tan, Ning
    Yatsuyanagi, Hiroya
    Mohan, Rajesh Elara
    Okayasu, Kazushige
    Nakamura, Akio
    2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (IEEE-ROBIO 2021), 2021, : 1190 - 1197
  • [37] Using a computer to track image quality and diagnostic error
    Dockray, KT
    Sawyer, JW
    Elks, ML
    Vines, DL
    AMERICAN JOURNAL OF ROENTGENOLOGY, 1997, 169 (03) : 635 - 636
  • [38] Using a Skeleton Gait Energy Image for Pathological Gait Classification
    Loureiro, Joao
    Correia, Paulo Lobato
    2020 15TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2020), 2020, : 503 - 507
  • [39] Pathological image analysis using the GPU: Stroma classification for neuroblastoma
    Ruiz, Antonio
    Sertel, Olcay
    Ujaldon, Manuel
    Catalyurek, Umit
    Saltz, Joel
    Gurcan, Metin
    2007 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, PROCEEDINGS, 2007, : 78 - +
  • [40] Image retrieval of rice varieties database using computer vision
    Huang, Xingyi
    Li, Jian
    Jiang, Song
    Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, 2005, 36 (10): : 94 - 96