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Abstract #2374

Breast Lesion Segmentation in MR Images through Knowledge Distillation-based Modality Speculation

Cheng Li1, Hui Sun1, Taohui Xiao1, Zaiyi Liu2, Qiegen Liu3, Xin Liu1, Hairong Zheng1, and Shanshan Wang1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China, 3Department of Electronic Information Engineering, Nanchang University, Nanchang, China

Multi-modal MR images are widely utilized to overcome the shortcomings of single modalities and pursue accurate image-based diagnoses. However, multi-modal MR imaging takes a longer time. For automated diagnosis, misalignment between modalities brings extra problems. To address these issues, we propose a new strategy to speculate the modality information by distillation-based knowledge transfer. Experiments on breast lesion segmentation confirm the feasibility of the proposed method. Networks trained with our method and single-modal MR image inputs can partially recover the breast lesion segmentation performance of models trained using two-modal MR images.

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