我们在我们内部数据集上进行训练并在公开数据集上对我们模型的性能进行一个评估从而说明我们对乳腺CEM影像先进行肿块的识别和分割在对肿块进行良恶性的分类的工作流程是有效的。虽然这些病例被标记为癌症病例但他们中的绝大多数都只有一个乳房患有乳腺癌无论是在内部数据集还是公开数据集上。因此我们使用的数据集包括恶性和非恶性图像阳性样本和阴性样本大致平衡。我们的数据集包括个病例每个病例包括4张减影图像LCCRCC

We trained our model on our internal dataset and evaluated its performance on a public dataset to demonstrate the effectiveness of our workflow for breast CEM image recognition and segmentation of tumors, followed by classification of benign and malignant tumors. Although these cases were labeled as cancer cases, the majority of them only had one breast affected by breast cancer, both in our internal dataset and the public dataset. Therefore, the dataset we used includes both malignant and non-malignant images, with a roughly balanced distribution of positive and negative samples. Our dataset consists of x cases, with each case having 4 contrast-enhanced images (LCC, RCC, LMLO, RMLO) and 2 low-energy images (CC and MLO views of the affected side). Hence, there are a total of x images, with x malignant and x non-malignant images. We employed random cropping and random translation as data augmentation techniques. We horizontally flipped all images of the right breast and resized all images to 224×224 before feeding them into the model