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Found 34 papers, 11 papers with code
Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional Prompts
Radiology reporting generative AI holds significant potential to alleviate clinical workloads and streamline medical care.
Deep Generative Models Unveil Patterns in Medical Images Through Vision-Language Conditioning
Deep generative models have significantly advanced medical imaging analysis by enhancing dataset size and quality.
Preserving Cardiac Integrity: A Topology-Infused Approach to Whole Heart Segmentation
Whole heart segmentation (WHS) supports cardiovascular disease (CVD) diagnosis, disease monitoring, treatment planning, and prognosis.
MONICA: Benchmarking on Long-tailed Medical Image Classification
We hope this codebase serves as a comprehensive and reproducible benchmark, encouraging further advancements in long-tailed medical image learning.
Coupling AI and Citizen Science in Creation of Enhanced Training Dataset for Medical Image Segmentation
Recent advancements in medical imaging and artificial intelligence (AI) have greatly enhanced diagnostic capabilities, but the development of effective deep learning (DL) models is still constrained by the lack of high-quality annotated datasets.
Beyond the Hype: A dispassionate look at vision-language models in medical scenario
RadVUQA mainly validates LVLMs across five dimensions: 1) Anatomical understanding, assessing the models' ability to visually identify biological structures; 2) Multimodal comprehension, which involves the capability of interpreting linguistic and visual instructions to produce desired outcomes; 3) Quantitative and spatial reasoning, evaluating the models' spatial awareness and proficiency in combining quantitative analysis with visual and linguistic information; 4) Physiological knowledge, measuring the models' capability to comprehend functions and mechanisms of organs and systems; and 5) Robustness, which assesses the models' capabilities against unharmonised and synthetic data.
CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression
Specifically, from the clinical prior knowledge, we tailor improvements to the traditional diffusion model and propose a Clinically-Informed Residual Diffusion model, called CIResDiff.
A dual-task mutual learning framework for predicting post-thrombectomy cerebral hemorrhage
Ischemic stroke is a severe condition caused by the blockage of brain blood vessels, and can lead to the death of brain tissue due to oxygen deprivation.
Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion
This paper presents a novel method for the conditional synthesis of IF images directly from BF images along with cell segmentation masks.
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method
In pulmonary tracheal segmentation, the scarcity of annotated data is a prevalent issue in medical segmentation.
Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation
Automatic lung organ segmentation on CT images is crucial for lung disease diagnosis.
Can Generative AI Replace Immunofluorescent Staining Processes? A Comparison Study of Synthetically Generated CellPainting Images from Brightfield
Cell imaging assays utilizing fluorescence stains are essential for observing sub-cellular organelles and their responses to perturbations.
Decoding Decision Reasoning: A Counterfactual-Powered Model for Knowledge Discovery
In medical imaging, particularly in early disease detection and prognosis tasks, discerning the rationale behind an AI model's predictions is crucial for evaluating the reliability of its decisions.
When AI Eats Itself: On the Caveats of AI Autophagy
Generative Artificial Intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech, and music.
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
Thus, we introduce a simple yet realistic method to generate thick CT images from thin-slice CT images, facilitating the creation of training pairs for SR algorithms.
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.
