Search Results for author: Yingying Fang

Found 9 papers, 3 papers with code

Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images

no code implementations5 Feb 2024 Xiaodan Xing, Huiyu Zhou, Yingying Fang, Guang Yang

AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world.

Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies

no code implementations29 Jan 2024 Jiahao Huang, Yinzhe Wu, Fanwen Wang, Yingying Fang, Yang Nan, Cagan Alkan, Lei Xu, Zhifan Gao, Weiwen Wu, Lei Zhu, Zhaolin Chen, Peter Lally, Neal Bangerter, Kawin Setsompop, Yike Guo, Daniel Rueckert, Ge Wang, Guang Yang

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans.

Federated Learning MRI Reconstruction

Dynamic Multimodal Information Bottleneck for Multimodality Classification

1 code implementation2 Nov 2023 Yingying Fang, Shuang Wu, Sheng Zhang, Chaoyan Huang, Tieyong Zeng, Xiaodan Xing, Simon Walsh, Guang Yang

Specifically, our information bottleneck module serves to filter out the task-irrelevant information and noises in the fused feature, and we further introduce a sufficiency loss to prevent dropping of task-relevant information, thus explicitly preserving the sufficiency of prediction information in the distilled feature.

Classification Medical Diagnosis +1

Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19

no code implementations24 Sep 2023 Yingying Fang, Xiaodan Xing, Shiyi Wang, Simon Walsh, Guang Yang

Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools.

Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers

no code implementations1 Apr 2022 Jiahao Huang, Yingying Fang, Yang Nan, Huanjun Wu, Yinzhe Wu, Zhifan Gao, Yang Li, Zidong Wang, Pietro Lio, Daniel Rueckert, Yonina C. Eldar, Guang Yang

Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e. g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning.

Anatomy Explainable Models +3

Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels

1 code implementation11 Feb 2022 Ming Li, Yingying Fang, Zeyu Tang, Chibudom Onuorah, Jun Xia, Javier Del Ser, Simon Walsh, Guang Yang

We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data.

Computed Tomography (CT) Decision Making +1

Swin Transformer for Fast MRI

2 code implementations10 Jan 2022 Jiahao Huang, Yingying Fang, Yinzhe Wu, Huanjun Wu, Zhifan Gao, Yang Li, Javier Del Ser, Jun Xia, Guang Yang

The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers.

MRI Reconstruction

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