Whole Slide Image Classification of Salivary Gland Tumours
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909293512491008 |
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| author | Charlton, John Alsanie, Ibrahim Khurram, Syed Ali |
| author_facet | Charlton, John Alsanie, Ibrahim Khurram, Syed Ali |
| contents | This work shows promising results using multiple instance learning on salivary gland tumours in classifying cancers on whole slide images. Utilising CTransPath as a patch-level feature extractor and CLAM as a feature aggregator, an F1 score of over 0.88 and AUROC of 0.92 are obtained for detecting cancer in whole slide images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12275 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Whole Slide Image Classification of Salivary Gland Tumours Charlton, John Alsanie, Ibrahim Khurram, Syed Ali Image and Video Processing Computer Vision and Pattern Recognition This work shows promising results using multiple instance learning on salivary gland tumours in classifying cancers on whole slide images. Utilising CTransPath as a patch-level feature extractor and CLAM as a feature aggregator, an F1 score of over 0.88 and AUROC of 0.92 are obtained for detecting cancer in whole slide images. |
| title | Whole Slide Image Classification of Salivary Gland Tumours |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.12275 |