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Found 17 papers, 2 papers with code
InDeed: Interpretable image deep decomposition with guaranteed generalizability
In this work, we introduce a novel framework for interpretable deep image decomposition, combining hierarchical Bayesian modeling and deep learning to create an architecture-modularized and model-generalizable deep neural network (DNN).
KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation
Knee osteoarthritis (OA) is the most common joint disorder and a leading cause of disability.
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis
The further experimental results in downstream tasks, 2D image segmentation and 3D image registration, indicate significant improvements resulting from DRDM, showcasing the potential of our model to advance image manipulation and synthesis in medical imaging and beyond.
MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging
MERIT enables uncertainty quantification of the predictions to enhance reliability, and employs a logic-based combination rule to improve interpretability.
Multi-Task Cooperative Learning via Searching for Flat Minima
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.
A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging
Therefore, we propose a reliable multi-view learning method with interpretable combination rules, which can model global representations to improve the accuracy of predictions.
Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels
In this work, we propose an unsupervised framework for multi-class segmentation with both intensity and shape constraints.
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior
This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets.
An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images
Diabetic retinopathy (DR) is a complication of diabetes, and one of the major causes of vision impairment in the global population.
Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation
In this work, we propose a new framework of distributed learning that bridges the gap between two groups, and improves the performance for both generic and local data.
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment.
Multi-Modality Cardiac Image Analysis with Deep Learning
Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease.
Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation
Unsupervised domain adaptation is useful in medical image segmentation.
Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information
As the target domain could be unknown, we randomly generate a modality vector for the target modality in the style transfer stage, to simulate the domain shift for unknown domains.
Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework
Compared with the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0. 01).
Atrial scars segmentation via potential learning in the graph-cuts framework
Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a routine scan for patients with atrial fibrillation (AF).
Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images
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) image, and the other is an anatomical MRI from the same acquisition session.
