# Whole Slide Image Classification of Salivary Gland Tumours

arXiv: https://arxiv.org/abs/2408.12275 · HTML: https://mlsanity.com/paper/2408.12275/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 22 Aug 2024.

## Builds on (most cited first, 1 of 1)

- [Micro-Net: A unified model for segmentation of various objects in microscopy images](https://mlsanity.com/paper/1804.08145/) (2018)

## Abstract (arXiv)

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.