论文标题
EMT网络：用于计算机辅助诊断乳腺癌的有效多任务网络
EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer
论文作者
论文摘要
深度学习的计算机辅助诊断已在乳腺癌检测中取得了前所未有的表现。但是，大多数方法是计算密集型的，这阻碍了它们在现实世界中的广泛传播。在这项工作中，我们提出了一种有效且轻巧的多任务学习体系结构，以同时对乳腺肿瘤进行分类和分割。我们将分割任务纳入肿瘤分类网络，这使骨干网络学习着集中在肿瘤区域上。此外，我们提出了一种新的数值稳定损失函数，可以轻松控制癌症检测的敏感性和特异性之间的平衡。使用具有1,511张图像的乳房超声数据集评估所提出的方法。肿瘤分类的准确性，灵敏度和特异性分别为88.6％，94.1％和85.3％。我们使用虚拟移动设备验证模型，平均推理时间为每个图像0.35秒。
Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In this work, we propose an efficient and light-weighted multitask learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. Moreover, we propose a new numerically stable loss function that easily controls the balance between the sensitivity and specificity of cancer detection. The proposed approach is evaluated using a breast ultrasound dataset with 1,511 images. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively. We validate the model using a virtual mobile device, and the average inference time is 0.35 seconds per image.
