The Diagnostic Classification of the Pathological Image Using Computer Vision
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 classif...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/2/96 |
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| author | Yasunari Matsuzaka Ryu Yashiro |
| author_facet | Yasunari Matsuzaka Ryu Yashiro |
| author_sort | Yasunari Matsuzaka |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9df2348eb9aa4de08704c97fa92bddd6 |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-9df2348eb9aa4de08704c97fa92bddd62025-08-20T03:11:06ZengMDPI AGAlgorithms1999-48932025-02-011829610.3390/a18020096The Diagnostic Classification of the Pathological Image Using Computer VisionYasunari Matsuzaka0Ryu Yashiro1Department of Microbiology and Immunology, Showa University School of Medicine, Tokyo 142-8555, JapanAdministrative Section of Radiation Protection, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-8551, JapanComputer 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.https://www.mdpi.com/1999-4893/18/2/96computer visiondeep learningconvolutional neural networksmedical imaging data |
| spellingShingle | Yasunari Matsuzaka Ryu Yashiro The Diagnostic Classification of the Pathological Image Using Computer Vision Algorithms computer vision deep learning convolutional neural networks medical imaging data |
| title | The Diagnostic Classification of the Pathological Image Using Computer Vision |
| title_full | The Diagnostic Classification of the Pathological Image Using Computer Vision |
| title_fullStr | The Diagnostic Classification of the Pathological Image Using Computer Vision |
| title_full_unstemmed | The Diagnostic Classification of the Pathological Image Using Computer Vision |
| title_short | The Diagnostic Classification of the Pathological Image Using Computer Vision |
| title_sort | diagnostic classification of the pathological image using computer vision |
| topic | computer vision deep learning convolutional neural networks medical imaging data |
| url | https://www.mdpi.com/1999-4893/18/2/96 |
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