Whole Slide Image Classification of Salivary Gland Tumours

John Charlton, Ibrahim Alsanie, Syed Ali Khurram
8/22/2024
eess.IVcs.CV

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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.

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Cite this paper

@article{charlton2024whole,
  title  = {Whole Slide Image Classification of Salivary Gland Tumours},
  author = {John Charlton and Ibrahim Alsanie and Syed Ali Khurram},
  year   = {2024},
  eprint = {2408.12275},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.12275v1}
}

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