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Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks.

, , , , , , , , , , and . DLMIA/ML-CDS@MICCAI, volume 10553 of Lecture Notes in Computer Science, page 151-159. Springer, (2017)

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Placenta accreta spectrum and hysterectomy prediction using MRI radiomic features., , , , , , , , , and 1 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation., , , , , , , and . BrainLes@MICCAI (2), volume 11993 of Lecture Notes in Computer Science, page 148-157. Springer, (2019)Deep-learning-based automatic segmentation of the placenta and uterine cavity on prenatal MR images., , , , , , , , , and 1 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 12465 of SPIE Proceedings, SPIE, (2023)Assessing reproducibility in magnetic resonance (MR) radiomics features between deep-learning segmented and expert manual segmented data and evaluating their diagnostic performance in pregnant women with suspected placenta accreta spectrum (PAS)., , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11597 of SPIE Proceedings, SPIE, (2021)CascadeNet for hysterectomy prediction in pregnant women due to placenta accreta spectrum., , , , , , , , , and 2 other author(s). Medical Imaging: Image Processing, volume 12032 of SPIE Proceedings, SPIE, (2022)Segmentation of uterus and placenta in MR images using a fully convolutional neural network., , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11314 of SPIE Proceedings, SPIE, (2020)Federated Learning for Brain Tumor Segmentation Using MRI and Transformers., , , , , , , , and . BrainLes@MICCAI (2), volume 12963 of Lecture Notes in Computer Science, page 444-454. Springer, (2021)Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks., , , , , , , , , and 1 other author(s). DLMIA/ML-CDS@MICCAI, volume 10553 of Lecture Notes in Computer Science, page 151-159. Springer, (2017)Automatic segmentation of uterine cavity and placenta on MR images using deep learning., , , , , , , , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 12036 of SPIE Proceedings, SPIE, (2022)QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results., , , , , , , , , and 82 other author(s). CoRR, (2021)