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Ultrasonography is an important routine examination for breast cancerdiagnosis, due to its non-invasive, radiation-free and low-cost properties.However, the diagnostic accuracy of breast cancer is still limited due to itsinherent limitations. It would be a tremendous success if we can preciselydiagnose breast cancer by breast ultrasound images (BUS). Many learning-basedcomputer-aided diagnostic methods have been proposed to achieve breast cancerdiagnosis/lesion classification. However, most of them require a pre-define ROIand then classify the lesion inside the ROI. Conventional classificationbackbones, such as VGG16 and ResNet50, can achieve promising classificationresults with no ROI requirement. But these models lack interpretability, thusrestricting their use in clinical practice. In this study, we propose a novelROI-free model for breast cancer diagnosis in ultrasound images withinterpretable feature representations. We leverage the anatomical priorknowledge that malignant and benign tumors have different spatial relationshipsbetween different tissue layers, and propose a HoVer-Transformer to formulatethis prior knowledge. The proposed HoVer-Trans block extracts the inter- andintra-layer spatial information horizontally and vertically. We conduct andrelease an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. Theproposed model is evaluated in three datasets by comparing with four CNN-basedmodels and two vision transformer models via five-fold cross validation. Itachieves state-of-the-art classification performance with the best modelinterpretability. In the meanwhile, our proposed model outperforms two seniorsonographers on the breast cancer diagnosis when only one BUS image is given.

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images