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Found 20 papers, 7 papers with code
Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images
AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world.
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients.
Dynamic Multimodal Information Bottleneck for Multimodality Classification
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.
Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation
This study proposes a pipeline that incorporates a novel style transfer model and a simultaneous super-resolution and segmentation model.
Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19
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.
SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis
Organoids are self-organized 3D cell clusters that closely mimic the architecture and function of in vivo tissues and organs.
Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach
This study presented a novel ultra-light-weight multi-modal deep-learning network to fuse EEG spectrogram imaging and temporal power vectors to enhance SD identification accuracy over each single electrode, allowing flexible EEG map and paving the way for SD detection on ultra-low-density EEG with variable electrode positioning.
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation
The generated images were then leveraged to train four distinct super-resolution (SR) models, which were subsequently evaluated using the real thick-slice images from the 2016 Low Dose CT Grand Challenge dataset.
You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images
In addition, we have empirically demonstrated that the utility score does not require images with both high fidelity and high variety.
The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus?
Training medical AI algorithms requires large volumes of accurately labeled datasets, which are difficult to obtain in the real world.
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations
To address these issues, in this work, we propose a novel strategy for medical image synthesis, namely Unsupervised Mask (UM)-guided synthesis, to obtain both synthetic images and segmentations using limited manual segmentation labels.
Swin Deformable Attention Hybrid U-Net for Medical Image Segmentation
Medical image segmentation is a crucial task in the field of medical image analysis.
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?
Our work is the first comparative study investigating the suitability of AE architecture for 3D CT SISR tasks and brings a rationale for researchers to re-think the choice of model architectures especially for 3D CT SISR tasks.
Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey
Data quality is the key factor for the development of trustworthy AI in healthcare.
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
especially for cohorts with different lung diseases.
Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI
The main obstacle is the computational cost of the self-attention layer, which is the core part of the Transformer, can be expensive for high resolution MRI inputs.
CS$^2$: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention
The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance.
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis
A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality.
Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation
Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis.
A Multi-scale Graph Network with Multi-head Attention for Histopathology Image Diagnosis
The proposed method is featured by a pyramid graph structure and an attention-based multi-instance learning strategy.
