Yuanhan Mo
According to our database1,
Yuanhan Mo
authored at least 21 papers
between 2017 and 2024.
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Bibliography
2024
Labelling with dynamics: A data-efficient learning paradigm for medical image segmentation.
Medical Image Anal., 2024
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis.
CoRR, 2024
Spinal Osteophyte Detection Via Robust Patch Extraction on Minimally Annotated X-Rays.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024
2023
VertXNet: An Ensemble Method for Vertebrae Segmentation and Identification of Spinal X-Ray.
CoRR, 2023
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops, 2023
2022
Medical Image Anal., 2022
CoRR, 2022
VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images.
CoRR, 2022
2021
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results.
CoRR, 2021
2020
CoRR, 2020
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020
Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided Survival Prediction.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020
2019
Proceedings of the Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data, 2019
SIMGAN: Photo-Realistic Semantic Image Manipulation Using Generative Adversarial Networks.
Proceedings of the 2019 IEEE International Conference on Image Processing, 2019
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2019
2018
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018
2017
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks.
Proceedings of the Medical Image Understanding and Analysis - 21st Annual Conference, 2017