ChengLong Deng1,2, BingChao Wu1,2, QingJun Wang3, QingLei Shi4, Bei Guan1,2, DaCheng Qu5, and YongJi Wang*1,2,6
1Collaborative Innovation Center, Institute of Software, Chinese Academy of Sciences, Beijing, China, 2University of Chinese Academy of Sciences, Beijing, China, 3Department of Radiology, PLA 6th medical center, Beijing, China, 4MR Scientific Marketing, Siemens Healthcare, Beijing, China, 5School of Computer Science and Technology， Beijing Institute of Technology, Beijing, China, 6State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
1Collaborative Innovation Center, Institute of Software, Chinese Academy of Sciences, Beijing, China, 2University of Chinese Academy of Sciences, Beijing, China, 3Department of Radiology, PLA 6th medical center, Beijing, China, 4MR Scientific Marketing, Siemens Healthcare, Beijing, China, 5School of Computer Science and Technology， Beijing Institute of Technology, Beijing, China, 6State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
Based on high b-value (2000 sec/mm2) DWI images, we optimize the Faster R-CNN model and studied the diagnostic performance of it, and a higher accuracy in differentiating benign and malignant thyroid micronodules was gained.
Figure 3: Schematic diagram of an optimized Faster R-CNN for automated detection and classification between HT and PTMC.
Figure 2: A papillary thyroid microcarcinoma (arrow-heads) in the right lobe (zoom in twice) on DW-MRIs and ADC maps with b=0,800,2000 sec/mm2.