Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound

• URL: http://arxiv.org/abs/2008.03435v1
• Date: Sat, 8 Aug 2020 03:42:00 GMT
• Title: Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound
• Authors: Wang Jian, Miao Juzheng, Yang Xin, Li Rui, Zhou Guangquan, Huang Yuhao, Lin Zehui, Xue Wufeng, Jia Xiaohong, Zhou Jianqiao, Huang Ruobing, Ni Dong
• Abstract summary: We propose 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. Results showed that the model scored a high classification accuracy of 95.4%, which indicates the efficiency of the proposed method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 set of multimodal images. Results showed that the model scored a high classification accuracy of 95.4%, which indicates the efficiency of the proposed method.

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