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Found 23 papers, 8 papers with code
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 Benchmark of Domain-Adapted Large Language Models for Generating Brief Hospital Course Summaries
To enable the adaptation of LLMs for BHC synthesis, we introduce a novel benchmark consisting of a pre-processed dataset extracted from MIMIC-IV notes, encapsulating clinical note, and brief hospital course (BHC) pairs.
CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation
However, developing FMs that can accurately interpret CXRs is challenging due to the (1) limited availability of large-scale vision-language datasets in the medical image domain, (2) lack of vision and language encoders that can capture the complexities of medical data, and (3) absence of evaluation frameworks for benchmarking the abilities of FMs on CXR interpretation.
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.
