On Image Search in Histopathology
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929285749538816 |
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| author | Tizhoosh, H. R. Pantanowitz, Liron |
| author_facet | Tizhoosh, H. R. Pantanowitz, Liron |
| contents | Pathology images of histopathology can be acquired from camera-mounted microscopes or whole slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this paper, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast and efficient image search methods in their work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08699 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Image Search in Histopathology Tizhoosh, H. R. Pantanowitz, Liron Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Information Retrieval Quantitative Methods Pathology images of histopathology can be acquired from camera-mounted microscopes or whole slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this paper, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast and efficient image search methods in their work. |
| title | On Image Search in Histopathology |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Information Retrieval Quantitative Methods |
| url | https://arxiv.org/abs/2401.08699 |