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
  • [1] Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
    Moldvai, Laszlo
    Mesterhazi, Peter Akos
    Teschner, Gergely
    Nyeki, Aniko
    APPLIED SCIENCES-BASEL, 2024, 14 (11):
  • [2] Impact of computer vision based secure image enrichment techniques on image classification model
    Rao, A. Shubha
    Mahantesh, K.
    JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY, 2023, 26 (03) : 899 - 911
  • [3] Sugarcane Classification for On-Site Assessment Using Computer Vision
    Kasempakdeepong, Piyapoj
    Ponchaiyapruek, Pondsulee
    Viriyothai, Pattamon
    Songchumrong, Anuwat
    Kantavat, Pittipol
    Pungprasertying, Prasertsak
    2022 17TH INTERNATIONAL JOINT SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND NATURAL LANGUAGE PROCESSING (ISAI-NLP 2022) / 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS (AIOT 2022), 2022,
  • [4] A Survey on Computer Vision Architectures for Large Scale Image Classification using Deep Learning
    Himabindu, D. Dakshayani
    Kumar, S. Praveen
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (10) : 105 - 120
  • [5] Automatic Gemstone Classification Using Computer Vision
    Chow, Bona Hiu Yan
    Reyes-Aldasoro, Constantino Carlos
    MINERALS, 2022, 12 (01)
  • [6] Workshop on Computer Vision for Bioanalytical Chemists: Classification and Detection of Amoebae Using Optical Microscopy Image Analysis with Machine Learning
    Zhang, Baosen
    Frkonja-Kuczin, Ariana
    Duan, Zhong-Hui
    Boika, Aliaksei
    JOURNAL OF CHEMICAL EDUCATION, 2023, : 539 - 545
  • [7] African bovid tribe classification using transfer learning and computer vision
    Dominguez-Rodrigo, Manuel
    Brophy, Juliet
    Mathews, Gregory J.
    Pizarro-Monzo, Marcos
    Baquedano, Enrique
    ANNALS OF THE NEW YORK ACADEMY OF SCIENCES, 2023, 1530 (01) : 152 - 160
  • [8] A computer vision approach for automated analysis and classification of microstructural image data
    DeCost, Brian L.
    Holm, Elizabeth A.
    COMPUTATIONAL MATERIALS SCIENCE, 2015, 110 : 126 - 133
  • [9] Cachaca Classification Using Chemical Features and Computer Vision
    Rodrigues, Bruno Urbano
    da Costa, Ronaldo Martins
    Salvini, Rogerio Lopes
    Soares, Anderson da Silva
    da Silva, Flavio Alves
    Caliari, Marcio
    Rodrigues Cardoso, Karla Cristina
    Monteiro Ribeiro, Tania Isabel
    2014 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, 2014, 29 : 2024 - 2033
  • [10] Defect Detection and Classification in Citrus Using Computer Vision
    Lopez, Jose J.
    Aguilera, Emanuel
    Cobos, Maximo
    NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS, 2009, 5864 : 11 - 18