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Found 19 papers, 4 papers with code
GEMA-Score: Granular Explainable Multi-Agent Score for Radiology Report Evaluation
However, existing evaluation metrics primarily assess the accuracy of key medical information coverage in generated reports compared to human-written reports, while overlooking crucial details such as the location and certainty of reported abnormalities.
Decoding Report Generators: A Cyclic Vision-Language Adapter for Counterfactual Explanations
Despite significant advancements in report generation methods, a critical limitation remains: the lack of interpretability in the generated text.
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.
Learning Task-Specific Sampling Strategy for Sparse-View CT Reconstruction
Thus, a task-specific sampling strategy can be applied for each type of scans to improve the quality of SVCT imaging and further assist in performance of downstream clinical usage.
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.
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.
Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation
Automatic lung organ segmentation on CT images is crucial for lung disease diagnosis.
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.
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.
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans.
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.
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.
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
especially for cohorts with different lung diseases.
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers
Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e. g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning.
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.
Swin Transformer for Fast MRI
The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers.
Pixel Screening Based Intermediate Correction for Blind Deblurring
Blind deblurring has attracted much interest with its wide applications in reality.
