Artificial Intelligence in Pathological Image Analysis
In routine pathological diagnosis, histopathological and cytopathological examination of specimens is conventionally performed under light microscopy. Whole slide images (WSIs) are the digitized counterparts of conventional glass slides obtained via specialized scanning devices. In recent years, the...
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| 格式: | Online |
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| 语言: | 英语 |
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MDPI - Multidisciplinary Digital Publishing Institute
2025
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| 在线阅读: | ONIX_20250220_9783725821419_133 |
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| _version_ | 1869530487089790976 |
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| collection | Directory of Open Access Books |
| description | In routine pathological diagnosis, histopathological and cytopathological examination of specimens is conventionally performed under light microscopy. Whole slide images (WSIs) are the digitized counterparts of conventional glass slides obtained via specialized scanning devices. In recent years, the introduction of digital pathology into clinical workflows such as intraoperative consultations and secondary consultations is increasing steadily. The advent of WSIs has led to the application of medical image analysis, machine learning, and deep learning approaches for aiding pathologists in inspecting WSIs and routine diagnosis. Deep learning in particular has found a wide array of applications (e.g., classification, segmentation, and patient outcome predictions) in computational pathology. In a time of distinct paradigm shifts and novel technological innovations, it is necessary for us to establish a unified comprehension(s) of artificial intelligence (AI) approaches in experimental and clinical pathology. In this Special Issue entitled “Artificial Intelligence in Pathological Image Analysis”, we collected a review and thirteen research articles in the areas of AI models in clinical and experimental pathology and computer vision in pathological image analysis. The published studies in this Special Issue provide great insights into the latest knowledge about the application of AI for pathological image analysis. |
| format | Online |
| id | doab-20.500.12854ir-152769 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1527692025-02-20T13:02:27Z Artificial Intelligence in Pathological Image Analysis Tsuneki, Masayuki histopathology cytopathology molecular pathology surgical pathology digital pathology microscopy whole slide image (WSI) clinical data computer vision deep learning machine learning computation mathematics domain adaptation segmentation classification pattern recognition explainable AI reconstruction thema EDItEUR::M Medicine and Nursing thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine In routine pathological diagnosis, histopathological and cytopathological examination of specimens is conventionally performed under light microscopy. Whole slide images (WSIs) are the digitized counterparts of conventional glass slides obtained via specialized scanning devices. In recent years, the introduction of digital pathology into clinical workflows such as intraoperative consultations and secondary consultations is increasing steadily. The advent of WSIs has led to the application of medical image analysis, machine learning, and deep learning approaches for aiding pathologists in inspecting WSIs and routine diagnosis. Deep learning in particular has found a wide array of applications (e.g., classification, segmentation, and patient outcome predictions) in computational pathology. In a time of distinct paradigm shifts and novel technological innovations, it is necessary for us to establish a unified comprehension(s) of artificial intelligence (AI) approaches in experimental and clinical pathology. In this Special Issue entitled “Artificial Intelligence in Pathological Image Analysis”, we collected a review and thirteen research articles in the areas of AI models in clinical and experimental pathology and computer vision in pathological image analysis. The published studies in this Special Issue provide great insights into the latest knowledge about the application of AI for pathological image analysis. 2025-02-20T13:02:25Z 2025-02-20T13:02:25Z 2024 book ONIX_20250220_9783725821419_133 9783725821419 9783725821426 https://directory.doabooks.org/handle/20.500.12854/152769 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/10011 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-2142-6 10.3390/books978-3-7258-2142-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725821419 9783725821426 266 Basel open access |
| spellingShingle | histopathology cytopathology molecular pathology surgical pathology digital pathology microscopy whole slide image (WSI) clinical data computer vision deep learning machine learning computation mathematics domain adaptation segmentation classification pattern recognition explainable AI reconstruction thema EDItEUR::M Medicine and Nursing thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine Artificial Intelligence in Pathological Image Analysis |
| title | Artificial Intelligence in Pathological Image Analysis |
| title_full | Artificial Intelligence in Pathological Image Analysis |
| title_fullStr | Artificial Intelligence in Pathological Image Analysis |
| title_full_unstemmed | Artificial Intelligence in Pathological Image Analysis |
| title_short | Artificial Intelligence in Pathological Image Analysis |
| title_sort | artificial intelligence in pathological image analysis |
| topic | histopathology cytopathology molecular pathology surgical pathology digital pathology microscopy whole slide image (WSI) clinical data computer vision deep learning machine learning computation mathematics domain adaptation segmentation classification pattern recognition explainable AI reconstruction thema EDItEUR::M Medicine and Nursing thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine |
| topic_facet | histopathology cytopathology molecular pathology surgical pathology digital pathology microscopy whole slide image (WSI) clinical data computer vision deep learning machine learning computation mathematics domain adaptation segmentation classification pattern recognition explainable AI reconstruction thema EDItEUR::M Medicine and Nursing thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine |
| url | ONIX_20250220_9783725821419_133 |