Search Results for author:
Found 18 papers, 11 papers with code
ViLMedic: a framework for research at the intersection of vision and language in medical AI
There is a growing need to model interactions between data modalities (e. g., vision, language) — both to improve AI predictions on existing tasks and to enable new applications.
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology.
Identifying Spurious Correlations using Counterfactual Alignment
We propose the counterfactual alignment method to detect and explore spurious correlations of black box classifiers.
ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data
The first key contribution of this work is to demonstrate through systematic evaluations that as the pairwise complexity of the training dataset increases, standard VLMs struggle to learn region-attribute relationships, exhibiting performance degradations of up to 37% on retrieval tasks.
The Effect of Counterfactuals on Reading Chest X-rays
This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays.
DDM$^2$: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique.
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays
Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent.
RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
We present evidence that the resulting model (RoentGen) is able to create visually convincing, diverse synthetic CXR images, and that the output can be controlled to a new extent by using free-form text prompts including radiology-specific language.
Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains
Multi-modal foundation models are typically trained on millions of pairs of natural images and text captions, frequently obtained through web-crawling approaches.
TorchXRayVision: A library of chest X-ray datasets and models
TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models.
SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric
Self-supervised learning (SSL) has become a popular pre-training tool due to its ability to capture generalizable and domain-specific feature representations of the underlying data for downstream tasks.
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
Designing Counterfactual Generators using Deep Model Inversion
Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting black-box models.
OncoNet: Weakly Supervised Siamese Network to automate cancer treatment response assessment between longitudinal FDG PET/CT examinations
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy.
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays
We also found that the Latent Shift explanation allows a user to have more confidence in true positive predictions compared to traditional approaches (0. 15$\pm$0. 95 in a 5 point scale with p=0. 01) with only a small increase in false positive predictions (0. 04$\pm$1. 06 with p=0. 57).
MRSaiFE: Tissue Heating Prediction for MRI: a Feasibility Study
We trained the software with a small database of image as a feasibility study and achieved successful proof of concept for both field strengths.
Open source software for automatic subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning
Subregional T2 values and four-year changes were calculated using a musculoskeletal radiologist's segmentations (Reader 1) and the model's segmentations.
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging
Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications.
