Interpretable Meningioma Grading and Segmentation with Multiparametric Deep Learning
Yohan Jun*1, Yae Won Park*2, Hyungseob Shin1, Yejee Shin1, Jeong Ryong Lee1, Kyunghwa Han2, Soo Mee Lim3, Seung-Koo Lee2, Sung Soo Ahn**2, and Dosik Hwang**1
1Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea, Republic of, 3Department of Radiology, Ewha Womans University College of Medicine, Seoul, Korea, Republic of
Preoperative prediction of meningioma grade is important because it influences treatment planning, including surgical resection and stereotactic radiosurgery strategy. The aim of this study was to establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation. We demonstrated that the interpretable multiparametric DL grading model that combined the T2-weighted and contrast-enhanced T1-weighted images can enable fully automatic grading of meningiomas along with segmentation.
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