1.3k citations · 2k across the 14 of their papers we have counts for
14 papers
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
Simulating time to event prediction with spatiotemporal echocardiography deep learning
Rohan Shad, Nicolas Quach, Robyn Fong +8
Integrating methods for time-to-event prediction with diagnostic imaging modalities is of considerable interest, as accurate estimates of survival requires accounting for censoring…
Medical Imaging and Machine Learning
Rohan Shad, John P. Cunningham, Euan A. Ashley +2
Advances in computing power, deep learning architectures, and expert labelled datasets have spurred the development of medical imaging artificial intelligence systems that rival cl…
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation
Yasuhide Miura, Yuhao Zhang, Emily Bao Tsai +2
Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying po…
Contrastive Learning of Medical Visual Representations from Paired Images and Text
Yuhao Zhang, Hang Jiang, Yasuhide Miura +2
Learning visual representations of medical images (e.g., X-rays) is core to medical image understanding but its progress has been held back by the scarcity of human annotations. Ex…
SCREENet: A Multi-view Deep Convolutional Neural Network for Classification of High-resolution Synthetic Mammographic Screening Scans
Saeed Seyyedi, Margaret J. Wong, Debra M. Ikeda +1
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 scree…
Deep Learning for the Digital Pathologic Diagnosis of Cholangiocarcinoma and Hepatocellular Carcinoma: Evaluating the Impact of a Web-based Diagnostic Assistant
Bora Uyumazturk, Amirhossein Kiani, Pranav Rajpurkar +17
While artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, the question of how best to incorporate these algorithms into clin…
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports
Yuhao Zhang, Derek Merck, Emily Bao Tsai +2
Neural abstractive summarization models are able to generate summaries which have high overlap with human references. However, existing models are not optimized for factual correct…
Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis
Okyaz Eminaga, Mahmoud Abbas, Christian Kunder +5
Different convolutional neural network (CNN) models have been tested for their application in histological image analyses. However, these models are prone to overfitting due to the…
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko +17
Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that conta…
Learning to Summarize Radiology Findings
Yuhao Zhang, Daisy Yi Ding, Tianpei Qian +2
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. Howev…
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Xuan Wang, Yu Zhang, Xiang Ren +5
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and dise…
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu +9
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural networ…
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
Pranav Rajpurkar, Jeremy Irvin, Aarti Bagul +11
We introduce MURA, a large dataset of musculoskeletal radiographs containing 40,561 images from 14,863 studies, where each study is manually labeled by radiologists as either norma…