EMT-NET: Efficient Multitask Network for Computer-Aided Diagnosis of Breast Cancer

作者：Jiaqiao Shi, Aleksandar Vakanski, Min Xian, Jianrui Ding, Chunping Ning · 发表于：2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) · 年份：2022 · DOI：10.1109/isbi52829.2022.9761438 · 被引用次数：15 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Digital Imaging for Blood Diseases

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 1511 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.