# Whole Slide Image Classification of Salivary Gland Tumours

Source: [Whole Slide Image Classification of Salivary Gland Tumours](<https://www.sota2.com/research/paper/68fa74911e209131cea6290a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:13:35.838Z

Authors: John Charlton, Ibrahim Alsanie, Syed Ali Khurram

Published: 2024-08-22T10:19:41.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2408.12275>)

[PDF](<https://arxiv.org/pdf/2408.12275.pdf>)