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Abstract #3186

Multi-class brain lesion detection: establishing a baseline for fastMRI+ dataset

Lifeng Mei1,2, Sixing Liu1,2, Guoxiong Deng1,2, Shaojun Liu1,2, Yali Zheng1,2, and Mengye Lyu1,2
1College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, 2College of Applied Sciences, Shenzhen University, Shenzhen, China


Computer aided diagnosis (CAD) is widely considered an important application of deep learning in healthcare. However, brain data with lesion location labels are rare in MRI domain. Recently, Microsoft Research has released a dataset of clinical pathology annotations based on the raw images from fastMRI and named it fastMRI+. Here, fastMRI+ brain dataset was analyzed and used to train deep learning-based models for lesion detection. Some well-known object detection architectures such as YOLOv3, Faster-RCNN and YOLOX were compared. Overall, this abstract established a baseline with improvement suggestions for future studies.

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