Search Results for author: Wenao Ma

Found 11 papers, 3 papers with code

Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Data

1 code implementation24 Jul 2023 Wenao Ma, Cheng Chen, Jill Abrigo, Calvin Hoi-Kwan Mak, Yuqi Gong, Nga Yan Chan, Chu Han, Zaiyi Liu, Qi Dou

Specifically, we propose to employ a variational autoencoder model to generate a low-dimensional prognostic score, which can effectively address the selection bias resulting from the non-randomized controlled trials.

Selection bias

3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation

1 code implementation23 Jun 2023 Shizhan Gong, Yuan Zhong, Wenao Ma, Jinpeng Li, Zhao Wang, Jingyang Zhang, Pheng-Ann Heng, Qi Dou

Notably, the original SAM architecture is designed for 2D natural images, therefore would not be able to extract the 3D spatial information from volumetric medical data effectively.

Image Segmentation Medical Image Segmentation +2

Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification

no code implementations1 Jan 2023 Shizhan Gong, Cheng Chen, Yuqi Gong, Nga Yan Chan, Wenao Ma, Calvin Hoi-Kwan Mak, Jill Abrigo, Qi Dou

Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage.

Decision Making Representation Learning

Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift

1 code implementation2 Jul 2022 Wenao Ma, Cheng Chen, Shuang Zheng, Jing Qin, Huimao Zhang, Qi Dou

In this paper, we propose the first method to tackle label shift for medical image classification, which effectively adapt the model learned from a single training label distribution to arbitrary unknown test label distribution.

Image Classification Medical Diagnosis +3

Rapid model transfer for medical image segmentation via iterative human-in-the-loop update: from labelled public to unlabelled clinical datasets for multi-organ segmentation in CT

no code implementations13 Apr 2022 Wenao Ma, Shuang Zheng, Lei Zhang, Huimao Zhang, Qi Dou

Despite the remarkable success on medical image analysis with deep learning, it is still under exploration regarding how to rapidly transfer AI models from one dataset to another for clinical applications.

Image Segmentation Medical Image Segmentation +2

Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation

no code implementations31 May 2021 Chenxin Li, Wenao Ma, Liyan Sun, Xinghao Ding, Yue Huang, Guisheng Wang, Yizhou Yu

In this paper, to address the above issues, we propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels, and enhance the spatial activation in those areas for the sub-type vessels.


Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification

no code implementations16 Mar 2021 Chenxin Li, Yunlong Zhang, Zhehan Liang, Wenao Ma, Yue Huang, Xinghao Ding

In this paper, we propose a novel vessel-mixing based consistency regularization framework, for cross-domain learning in retinal A/V classification.

Classification General Classification

Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification

no code implementations18 Jul 2020 Wenao Ma, Shuang Yu, Kai Ma, Jiexiang Wang, Xinghao Ding, Yefeng Zheng

In this paper, we propose a multi-task deep neural network with spatial activation mechanism that is able to segment full retinal vessel, artery and vein simultaneously, without the pre-requirement of vessel segmentation.

Classification General Classification +2

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

no code implementations4 Jul 2020 Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying WEI, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang

Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.

Brain Segmentation

Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network

no code implementations28 Oct 2019 Jiexiang Wang, Hongyu Huang, Chaoqi Chen, Wenao Ma, Yue Huang, Xinghao Ding

Automatic and accurate segmentation of the ventricles and myocardium from multi-sequence cardiac MRI (CMR) is crucial for the diagnosis and treatment management for patients suffering from myocardial infarction (MI).

Domain Adaptation Management +1

Uncertainty-Guided Domain Alignment for Layer Segmentation in OCT Images

no code implementations22 Aug 2019 Jiexiang Wang, Cheng Bian, Meng Li, Xin Yang, Kai Ma, Wenao Ma, Jin Yuan, Xinghao Ding, Yefeng Zheng

Automatic and accurate segmentation for retinal and choroidal layers of Optical Coherence Tomography (OCT) is crucial for detection of various ocular diseases.


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