10 citations · 25 across the 17 of their papers we have counts for
17 papers
ReCBM: Uncertainty-Gated Relational Reasoning for Concept Bottleneck Models
An Sui, Yuzhu Li, Fuping Wu +1
Concept Bottleneck Models (CBMs) provide an interpretable framework by grounding predictions in human-understandable concepts, enabling semantic inspection and test-time interventi…
Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation
An Sui, Yuzhu Li, Gunter Schumann +2
Uncertainty quantification complements model predictions by characterizing their reliability, which is essential for high-stakes decision making such as medical image segmentation.…
Uncertainty-Supervised Interpretable and Robust Evidential Segmentation
Yuzhu Li, An Sui, Fuping Wu +1
Uncertainty estimation has been widely studied in medical image segmentation as a tool to provide reliability, particularly in deep learning approaches. However, previous methods g…
InDeed: Interpretable image deep decomposition with guaranteed generalizability
Sihan Wang, Shangqi Gao, Fuping Wu +1
Image decomposition aims to analyze an image into elementary components, which is essential for numerous downstream tasks and also by nature provides certain interpretability to th…
MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging
Yuanye Liu, Zheyao Gao, Nannan Shi +4
Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a specific sub-region, multi-vie…
A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging
Zheyao Gao, Yuanye Liu, Fuping Wu +3
Staging of liver fibrosis is important in the diagnosis and treatment planning of patients suffering from liver diseases. Current deep learning-based methods using abdominal magnet…
Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels
Sihan Wang, Fuping Wu, Lei Li +3
Cardiac segmentation is in great demand for clinical practice. Due to the enormous labor of manual delineation, unsupervised segmentation is desired. The ill-posed optimization pro…
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior
Kaiwen Wan, Lei Li, Dengqiang Jia +9
Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image…
An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images
Yuhan Zheng, Fuping Wu, Bartłomiej W. Papież
Diabetic retinopathy (DR) is a complication of diabetes, and one of the major causes of vision impairment in the global population. As the early-stage manifestation of DR is usuall…
Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation
Zheyao Gao, Lei Li, Fuping Wu +2
Distributed learning has shown great potential in medical image analysis. It allows to use multi-center training data with privacy protection. However, data distributions in local…
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Lei Li, Fuping Wu, Sihan Wang +29
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardiu…
Multi-Modality Cardiac Image Analysis with Deep Learning
Lei Li, Fuping Wu, Sihang Wang +1
Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease. Late gadolinium enhancement magnetic…
Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation
Fuping Wu, Xiahai Zhuang
Unsupervised domain adaptation is useful in medical image segmentation. Particularly, when ground truths of the target images are not available, domain adaptation can train a targe…
Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information
Lei Li, Veronika A. Zimmer, Wangbin Ding +4
Deep learning (DL)-based models have demonstrated good performance in medical image segmentation. However, the models trained on a known dataset often fail when performed on an uns…
Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework
Lei Li, Fuping Wu, Guang Yang +6
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the…
Atrial scars segmentation via potential learning in the graph-cuts framework
Lei Li, Fuping Wu, Guang Yang +6
Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a routine scan for patients with atrial fibrillation (AF). However, due to the low image quality automat…
Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images
Fuping Wu, Lei Li, Guang Yang +6
We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE)…