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.

翻译：本研究展示了在多实例学习框架下对唾液腺肿瘤全切片图像进行癌症分类的显著成果。通过采用CTransPath作为图像块级特征提取器，并利用CLAM作为特征聚合器，在全切片图像癌症检测任务中取得了超过0.88的F1分数和0.92的AUROC值。