Abstract
Breast cancer is the most common invasive cancer in women. Besides the primary B-mode ultrasound screening, sonographers have explored the inclusion of Doppler, strain and shear-wave elasticity imaging to advance the diagnosis. However, recognizing useful patterns in all types of images and weighing up the significance of each modality can elude less-experienced clinicians. In this paper, we explore, for the first time, an automatic way to combine the four types of ultrasonography to discriminate between benign and malignant breast nodules. A novel multimodal network is proposed, along with promising learnability and simplicity to improve classification accuracy. The key is using a weight-sharing strategy to encourage interactions between modalities and adopting an additional cross-modalities objective to integrate global information. In contrast to hardcoding the weights of each modality in the model, we embed it in a Reinforcement Learning framework to learn this weighting in an end-to-end manner. Thus the model is trained to seek the optimal multimodal combination without handcrafted heuristics. The proposed framework is evaluated on a dataset contains 1616 sets of multimodal images. Results showed that the model scored a high classification accuracy of 95.4%, which indicates the efficiency of the proposed method.
J. Wang and J. Miao—Contribute equally to this work.
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Acknowledgement
This work was supported by National Key R&D Program of China (No. 2019YFC 0118300); Shenzhen Peacock Plan (No. KQTD2016053112051497, KQJSCX2018 0328095606003); Medical Scientific Research Foundation of Guangdong Province, China (No. B2018031); National Natural Science Foundation of China (Project No. NSFC61771130).
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Wang, J. et al. (2020). Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_19
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DOI: https://doi.org/10.1007/978-3-030-59725-2_19
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