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Found 16 papers, 5 papers with code
Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation
Fibrotic Lung Disease (FLD) is a severe condition marked by lung stiffening and scarring, leading to respiratory decline.
Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks
The manifestation of symptoms associated with lung diseases can vary in different depths for individual patients, highlighting the significance of 3D information in CT scans for medical image classification.
DiffExplainer: Unveiling Black Box Models Via Counterfactual Generation
To address this limitation, we propose an agent model capable of generating counterfactual images that prompt different decisions when plugged into a black box model.
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.
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.
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation
We propose a novel Deep Active Learning (DeepAL) model-3D Wasserstein Discriminative UNet (WD-UNet) for reducing the annotation effort of medical 3D Computed Tomography (CT) segmentation.
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.
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.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
Adversarial Transformer for Repairing Human Airway Segmentation
Discontinuity in the delineation of peripheral bronchioles hinders the potential clinical application of automated airway segmentation models.
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.
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.
Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels
We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data.
Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness.
