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Found 19 papers, 13 papers with code
Learning to Summarize Radiology Findings
The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians.
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries.
Ranked #1 on
Multi-Label Classification
on MRNet
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies.
Ranked #95 on
Multi-Label Classification
on CheXpert
Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis
A well-fitted PlexusNet-based model delivered comparable classification performance (AUC: 0. 963) in distinguishing prostate cancer from healthy tissues, although it was at least 23 times smaller, had a better model calibration and clinical utility than the comparison models.
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports
Neural abstractive summarization models are able to generate summaries which have high overlap with human references.
Video-based AI for beat-to-beat assessment of cardiac function
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness.
Ranked #2 on
on Echonet-Dynamic
Biomedical and Clinical English Model Packages in the Stanza Python NLP Library
We introduce biomedical and clinical English model packages for the Stanza Python NLP library.
SCREENet: A Multi-view Deep Convolutional Neural Network for Classification of High-resolution Synthetic Mammographic Screening Scans
Purpose: To develop and evaluate the accuracy of a multi-view deep learning approach to the analysis of high-resolution synthetic mammograms from digital breast tomosynthesis screening cases, and to assess the effect on accuracy of image resolution and training set size.
Contrastive Learning of Medical Visual Representations from Paired Images and Text
Existing work commonly relies on fine-tuning weights transferred from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize.
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation
We further show via a human evaluation and a qualitative analysis that our system leads to generations that are more factually complete and consistent compared to the baselines.
Medical Imaging and Machine Learning
Advances in computing power, deep learning architectures, and expert labelled datasets have spurred the development of medical imaging artificial intelligence systems that rival clinical experts in a variety of scenarios.
Simulating time to event prediction with spatiotemporal echocardiography deep learning
Integrating methods for time-to-event prediction with diagnostic imaging modalities is of considerable interest, as accurate estimates of survival requires accounting for censoring of individuals within the observation period.
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14, 579 entities and 10, 889 relations), and a test dataset, which contains two independent sets of board-certified radiologist annotations for 100 radiology reports split equally across the MIMIC-CXR and CheXpert datasets.
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.
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards
To overcome this limitation, we propose a new method, the RadGraph reward, to further improve the factual completeness and correctness of generated radiology reports.
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.
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS).
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.
Training Small Multimodal Models to Bridge Biomedical Competency Gap: A Case Study in Radiology Imaging
Frontier models such as GPT-4V still have major competency gaps in multimodal capabilities for biomedical applications.
