The purpose of this study was to establish a robust interpretable deep learning (DL) model for the automatic noninvasive grading of meningiomas along with segmentation. Over 250 meningioma patients who underwent a preoperative brain MRI, including T2-weighted (T2) and contrast-enhanced T1-weighted (T1C) images, were included in the training set. The authors were able to determine that an interpretable multiparametric DL model combining T1C and T2 can enable the fully automatic grading of meningiomas along with segmentation. Key points The multiparametric DL model showed robustness in grading and segmentation on external validation. The diagnostic performance of the combined DL grading model was higher than that of the human readers. The RCAM interpreted that DL grading model recognized the meaningful features at the tumor margin for grading. Article: Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning Authors: Yohan Jun, Yae Won Park, Hyungseob Shin, Yejee Shin, Jeong Ryong Lee, Kyunghwa Han, Sung Soo Ahn, Soo Mee Lim, Dosik Hwang & Seung-Koo Lee
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
