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Found 31 papers, 14 papers with code
Best Practices for Large Language Models in Radiology
At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights.
Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis
We explored methods like using a proxy model and using radiologist feedback to improve the quality of synthetic data.
Foundation Models in Radiology: What, How, When, Why and Why Not
Given the potentially transformative impact that foundation models can have on the field of radiology, this review aims to establish a standardized terminology concerning foundation models, with a specific focus on the requirements of training data, model training paradigms, model capabilities, and evaluation strategies.
Highly efficient non-rigid registration in k-space with application to cardiac Magnetic Resonance Imaging
The accelerated scans in such applications result in imaging artifacts that compromise the motion estimation.
Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging
Medical imaging cohorts are often confounded by factors such as acquisition devices, hospital sites, patient backgrounds, and many more.
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imaging
To address these limitations, we propose to embed information from multiple domains, including low-rank, image, and k-space, in a novel deep learning network for MRI reconstruction, which we denote as A-LIKNet.
Merlin: A Vision Language Foundation Model for 3D Computed Tomography
However, current medical VLMs are generally limited to 2D images and short reports, and do not leverage electronic health record (EHR) data for supervision.
Attention-aware non-rigid image registration for accelerated MR imaging
The proposed method was evaluated on in-house acquired fully sampled and accelerated data of 101 patients and 62 healthy subjects undergoing cardiac and thoracic MRI.
Unlocking Robust Segmentation Across All Age Groups via Continual Learning
Most deep learning models in medical imaging are trained on adult data with unclear performance on pediatric images.
A Dataset and Benchmark for Hospital Course Summarization with Adapted Large Language Models
Furthermore, we introduce a benchmark of the summarization performance of two general-purpose LLMs and three healthcare-adapted LLMs.
A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
The CheXagent-drafted reports improved the writing efficiency of both radiology residents and attending radiologists in 81% and 61% of cases, respectively, without loss of quality.
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care.
Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations
Deep Learning (DL) methods have shown promising results for solving ill-posed inverse problems such as MR image reconstruction from undersampled $k$-space data.
Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI
In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonance (MR) images to develop Masked-LMCTrans, a longitudinal multi-modality co-attentional CNN-Transformer that provides interaction and joint reasoning between serial PET/MRs of the same patient.
LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance Imaging
Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts.
Uncertainty-Guided Progressive GANs for Medical Image Translation
However, the state-of-the-art GAN-based frameworks do not estimate the uncertainty in the predictions made by the network that is essential for making informed medical decisions and subsequent revision by medical experts and has recently been shown to improve the performance and interpretability of the model.
Uncertainty-Based Biological Age Estimation of Brain MRI Scans
We apply the proposed methodology on a brain MRI dataset containing healthy individuals as well as Alzheimer's patients.
Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation
Our study offers valuable guidelines and outlines practical conditions under which insights derived from synthetic medical images are similar to those that would have been derived from real imaging data.
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation
Thus, a whole-body assessment of the BA does not reflect the deviations of aging behavior between organs.
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies
Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions.
ipA-MedGAN: Inpainting of Arbitrary Regions in Medical Imaging
Local deformations in medical modalities are common phenomena due to a multitude of factors such as metallic implants or limited field of views in magnetic resonance imaging (MRI).
Organ-based Chronological Age Estimation based on 3D MRI Scans
Individuals age differently depending on a multitude of different factors such as lifestyle, medical history and genetics.
Unsupervised Adversarial Correction of Rigid MR Motion Artifacts
Motion is one of the main sources for artifacts in magnetic resonance (MR) images.
Unsupervised Medical Image Translation Using Cycle-MedGAN
The proposed framework utilizes new non-adversarial cycle losses which direct the framework to minimize the textural and perceptual discrepancies in the translated images.
Adversarial Inpainting of Medical Image Modalities
Numerous factors could lead to partial deteriorations of medical images.
Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs
Motion artifacts are a primary source of magnetic resonance (MR) image quality deterioration with strong repercussions on diagnostic performance.
Deep Shape Analysis on Abdominal Organs for Diabetes Prediction
Morphological analysis of organs based on images is a key task in medical imaging computing.
A Machine-learning framework for automatic reference-free quality assessment in MRI
Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest.
MedGAN: Medical Image Translation using GANs
Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications.
