1.9k citations · 6.6k across the 25 of their papers we have counts for
25 papers
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders
Shawn Xu, Lin Yang, Christopher Kelly +25
In this work, we present an approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or grafted onto…
Towards Generalist Biomedical AI
Tao Tu, Shekoofeh Azizi, Danny Driess +29
Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly en…
: A medical image annotation framework based on large vision model
Chenglong Wang, Dexuan Li, Sucheng Wang +4
Recently, large vision model, Segment Anything Model (SAM), has revolutionized the computer vision field, especially for image segmentation. SAM presented a new promptable segmenta…
Towards Expert-Level Medical Question Answering with Large Language Models
Karan Singhal, Tao Tu, Juraj Gottweis +28
Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason o…
Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning
Wei-Hung Weng, Sebastien Baur, Mayank Daswani +13
Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in t…
MONAI: An open-source framework for deep learning in healthcare
M. Jorge Cardoso, Wenqi Li, Richard Brown +53
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagn…
Discovering novel systemic biomarkers in photos of the external eye
Boris Babenko, Ilana Traynis, Christina Chen +16
External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about…
Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis
Chenglong Wang, Yun Liu, Fen Wang +4
Lung cancer has the highest mortality rate of deadly cancers in the world. Early detection is essential to treatment of lung cancer. However, detection and accurate diagnosis of pu…
Deep learning for detecting pulmonary tuberculosis via chest radiography: an international study across 10 countries
Sahar Kazemzadeh, Jin Yu, Shahar Jamshy +21
Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO recommends chest radiographs (CXRs) for TB screening, the limited availability of CXR interpretation is a bar…
Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading
Ellery Wulczyn, Kunal Nagpal, Matthew Symonds +20
Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial i…
Detecting hidden signs of diabetes in external eye photographs
Boris Babenko, Akinori Mitani, Ilana Traynis +10
Diabetes-related retinal conditions can be detected by examining the posterior of the eye. By contrast, examining the anterior of the eye can reveal conditions affecting the front…
Interpretable Survival Prediction for Colorectal Cancer using Deep Learning
Ellery Wulczyn, David F. Steiner, Melissa Moran +20
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) f…
Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases
Zaid Nabulsi, Andrew Sellergren, Shahar Jamshy +20
Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific…
Predicting Risk of Developing Diabetic Retinopathy using Deep Learning
Ashish Bora, Siva Balasubramanian, Boris Babenko +13
Diabetic retinopathy (DR) screening is instrumental in preventing blindness, but faces a scaling challenge as the number of diabetic patients rises. Risk stratification for the dev…
Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3
Zizhao He, Xinzhong Er, Qian Long +6
Convolutional Neutral Networks have been successfully applied in searching for strong lensing systems, leading to discoveries of new candidates from large surveys. On the other han…
Deep learning-based survival prediction for multiple cancer types using histopathology images
Ellery Wulczyn, David F. Steiner, Zhaoyang Xu +7
Prognostic information at diagnosis has important implications for cancer treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and c…
A deep learning system for differential diagnosis of skin diseases
Yuan Liu, Ayush Jain, Clara Eng +19
Skin conditions affect an estimated 1.9 billion people worldwide. A shortage of dermatologists causes long wait times and leads patients to seek dermatologic care from general prac…
Detecting Anemia from Retinal Fundus Images
Akinori Mitani, Yun Liu, Abigail Huang +5
Despite its high prevalence, anemia is often undetected due to the invasiveness and cost of screening and diagnostic tests. Though some non-invasive approaches have been developed,…
Similar Image Search for Histopathology: SMILY
Narayan Hegde, Jason D. Hipp, Yun Liu +11
The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Thou…
Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection
Timo Kohlberger, Yun Liu, Melissa Moran +6
Digital pathology enables remote access or consults and powerful image analysis algorithms. However, the slide digitization process can create artifacts such as out-of-focus (OOF).…
Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration
Po-Hsuan Cameron Chen, Krishna Gadepalli, Robert MacDonald +7
The brightfield microscope is instrumental in the visual examination of both biological and physical samples at sub-millimeter scales. One key clinical application has been in canc…
Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photos
Sonia Phene, R. Carter Dunn, Naama Hammel +17
Glaucoma is the leading cause of preventable, irreversible blindness world-wide. The disease can remain asymptomatic until severe, and an estimated 50%-90% of people with glaucoma…
Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer
Kunal Nagpal, Davis Foote, Yun Liu +17
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scorin…
Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning
Ryan Poplin, Avinash V. Varadarajan, Katy Blumer +5
Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses. However, observing and quantifying associations in im…