Whole Slide Image Classification of Salivary Gland Tumours
arXiv:2408.12275v11 citationsh-index: 27
Originality Synthesis-oriented
AI Analysis
This work addresses cancer diagnosis in medical imaging for salivary gland tumours, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of classifying salivary gland tumours from whole slide images using multiple instance learning, achieving an F1 score over 0.88 and AUROC of 0.92 for cancer detection.
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.