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Whole Slide Image Classification of Salivary Gland Tumours

Image and Video Processing 2024-08-23 v1 Computer Vision and Pattern Recognition
Authors: John Charlton , Ibrahim Alsanie , Syed Ali Khurram
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Abstract

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.

Keywords

whole slide image multiple instance learning salivary gland tumors CTransPath CLAM

Cite

@article{arxiv.2408.12275,
  title  = {Whole Slide Image Classification of Salivary Gland Tumours},
  author = {John Charlton and Ibrahim Alsanie and Syed Ali Khurram},
  journal= {arXiv preprint arXiv:2408.12275},
  year   = {2024}
}

Comments

5 pages, 2 figures, 28th UK Conference on Medical Image Understanding and Analysis - clinical abstract

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