Search Results for author: Yuanhan Mo

Found 15 papers, 0 papers with code

Spinal Osteophyte Detection via Robust Patch Extraction on minimally annotated X-rays

no code implementations29 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.

Multi-Task Cooperative Learning via Searching for Flat Minima

no code implementations21 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.

Multi-Task Learning

Towards Automatic Scoring of Spinal X-ray for Ankylosing Spondylitis

no code implementations8 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.

VertXNet: An Ensemble Method for Vertebrae Segmentation and Identification of Spinal X-Ray

no code implementations7 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.

Segmentation

VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images

no code implementations12 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.

Segmentation

Suggestive Annotation of Brain MR Images with Gradient-guided Sampling

no code implementations2 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.

Brain Segmentation Image Segmentation +2

Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling

no code implementations26 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.

BIG-bench Machine Learning Image Segmentation +2

Deep Generative Model-based Quality Control for Cardiac MRI Segmentation

no code implementations23 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.

Image Segmentation MRI segmentation +2

Efficient Deep Representation Learning by Adaptive Latent Space Sampling

no code implementations19 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.

General Classification Image Classification +2

Suggestive Labelling for Medical Image Analysis by Adaptive Latent Space Sampling

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.

Informativeness

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

no code implementations10 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.

Brain Tumor Segmentation Image Segmentation +2

The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation

no code implementations27 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.

Left Ventricle Segmentation LV Segmentation +1

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