Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, it is still not the first-line screening test for breast cancer due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&GYFYY for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and two vision transformer models via a five-fold cross validation. It achieves state-of-the-art classification performance with the best model interpretability.
翻译:超声波分析是乳腺癌诊断的一个重要常规检查,原因是其非侵入性、无辐射和低成本的特性。然而,由于其内在局限性,它仍不是乳腺癌的第一线筛选测试。如果我们能够精确地通过乳房超声图像诊断乳腺癌(BUS),它将是一个巨大的成功。我们提出了许多基于学习的计算机辅助诊断方法,以实现乳腺癌诊断/感官分类。然而,其中多数方法需要事先确定性能模型,然后对ROI内部的跨值进行分类。常规分类支柱,如VGG16和ResNet50等,可以在没有ROI要求的情况下实现有希望的分类结果。但是这些模型缺乏可解释性,从而限制了其在临床实践中的使用。在本研究中,我们提出了一个新的无乳腺癌诊断模型,在超声波图像中进行解释性特征描述。我们利用了先前的解剖学学学知识,即恶性肿瘤和良性肿瘤模型在不同组织层之间有着不同的空间关系,并提议采用状态解析法来编制这一先前的知识。拟议中的HOVer-Trans-Trading-Tradef-trainal Ex-deal-dealal-deal-deal-deal-degraphal-deal-deal-deal-deal-deal-deal disal disal-dal-dal-dal-dal-dal-deal-deal-dal-deal-deal-deal-dal-dal-dal-dal-dal-dal-dal-dal-dal-dal-dal-dal-dal-dal-I-dal-Iversal-dal-Ial-d-d-I-d-d-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-Ial-I-Ial-Ial-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-