no code implementations • 29 Feb 2024 • Soumya Snigdha Kundu, Yuanhan Mo, Nicharee Srikijkasemwat, Bartłomiej W. Papiez
The development and progression of arthritis is strongly associated with osteophytes, which are small and elusive bone growths.
no code implementations • 21 Sep 2023 • Fuping Wu, Le Zhang, Yang Sun, Yuanhan Mo, Thomas Nichols, Bartlomiej W. Papiez
In this work, we propose to formulate MTL as a multi/bi-level optimization problem, and therefore force features to learn from each task in a cooperative approach.
no code implementations • 8 Aug 2023 • Yuanhan Mo, Yao Chen, Aimee Readie, Gregory Ligozio, Thibaud Coroller, Bartłomiej W. Papież
In this study, we address this challenge by prototyping a 2-step auto-grading pipeline, called VertXGradeNet, to automatically predict mSASSS scores for the cervical and lumbar vertebral units (VUs) in X-ray spinal imaging.
no code implementations • 7 Feb 2023 • Yao Chen, Yuanhan Mo, Aimee Readie, Gregory Ligozio, Indrajeet Mandal, Faiz Jabbar, Thibaud Coroller, Bartlomiej W. Papiez
Our experimental results have shown that the proposed pipeline outperformed two SOTA segmentation models on our test dataset (MEASURE 1) with a mean Dice of 0. 90, vs. a mean Dice of 0. 73 for Mask R-CNN and 0. 72 for U-Net.
no code implementations • 12 Oct 2022 • Shuo Wang, Chen Qin, Chengyan Wang, Kang Wang, Haoran Wang, Chen Chen, Cheng Ouyang, Xutong Kuang, Chengliang Dai, Yuanhan Mo, Zhang Shi, Chenchen Dai, Xinrong Chen, He Wang, Wenjia Bai
The quality of cardiac magnetic resonance (CMR) imaging is susceptible to respiratory motion artifacts.
no code implementations • 12 Jul 2022 • Yao Chen, Yuanhan Mo, Aimee Readie, Gregory Ligozio, Thibaud Coroller, Bartlomiej W. Papiez
Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations.
no code implementations • 2 Jun 2022 • Chengliang Dai, Shuo Wang, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai
We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation.
no code implementations • 26 Jun 2020 • Chengliang Dai, Shuo Wang, Yuanhan Mo, Kaichen Zhou, Elsa Angelini, Yike Guo, Wenjia Bai
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks.
no code implementations • 23 Jun 2020 • Shuo Wang, Giacomo Tarroni, Chen Qin, Yuanhan Mo, Chengliang Dai, Chen Chen, Ben Glocker, Yike Guo, Daniel Rueckert, Wenjia Bai
Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.
no code implementations • 19 Mar 2020 • Yuanhan Mo, Shuo Wang, Chengliang Dai, Rui Zhou, Zhongzhao Teng, Wenjia Bai, Yike Guo
Supervised deep learning requires a large amount of training samples with annotations (e. g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain.
no code implementations • MIDL 2019 • Yuanhan Mo, Shuo Wang, Chengliang Dai, Zhongzhao Teng, Wenjia Bai, Yike Guo
Supervised deep learning for medical imaging analysis requires a large amount of training samples with annotations (e. g. label class for classification task, pixel- or voxel-wised label map for medical segmentation tasks), which are expensive and time-consuming to obtain.
no code implementations • 19 Nov 2019 • Shuo Wang, Chengliang Dai, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity.
no code implementations • 28 Aug 2019 • Chengliang Dai, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai
Brain MR image segmentation is a key task in neuroimaging studies.
no code implementations • 10 May 2017 • Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo
In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent.
no code implementations • 27 Mar 2017 • Yuanhan Mo, Fangde Liu, Douglas McIlwraith, Guang Yang, Jingqing Zhang, Taigang He, Yike Guo
Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge.