In healthcare, it is essential to explain the decision-making process of machine learning models to establish the trustworthiness of clinicians. This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice. The employed features include the BI-RADS descriptors of shape, orientation, margin, echo pattern, and posterior features. Additionally, our approach predicts the likelihood of malignancy of the findings, which relates to the BI-RADS assessment category reported by clinicians. Experimental validation on a dataset consisting of 1,192 images indicates improved model accuracy, supported by explanations in clinical terms using the BI-RADS lexicon.

翻译：在保健方面,必须解释机器学习模型的决策过程,以确定临床医生的可信度。本文介绍了BI-RADS-Net,这是在乳房超声波图像中检测癌症的一种新颖的深层次学习方法。拟议方法包含通过与临床诊断有关的学习特征描述解释和分类乳腺癌的任务。从临床实践中临床医生用于诊断和报告的形态特征的角度解释了预测(恶性或恶性),所使用的特征包括BI-RADS的形状、方向、边距、回声模式和外表特征的描述。此外,我们的方法预测了这些结果的恶性可能性,这与临床医生报告的BI-RADS评估类别有关。对由1,192个图像组成的数据集的实验性验证表明模型准确性得到了改进,并辅之以使用BI-RADS词汇的临床术语解释。