تقرير
Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound
| العنوان: | Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound |
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| المؤلفون: | Jian, Wang, Juzheng, Miao, Xin, Yang, Rui, Li, Guangquan, Zhou, Yuhao, Huang, Zehui, Lin, Wufeng, Xue, Xiaohong, Jia, Jianqiao, Zhou, Ruobing, Huang, Dong, Ni |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | 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. Comment: Early Accepted by MICCAI 2020 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2008.03435 |
| رقم الأكسشن: | edsarx.2008.03435 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |