568 citations · 751 across the 32 of their papers we have counts for
32 papers
Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting
Shantanu Ghosh, Vedant Parthesh Joshi, Rayan Syed +14
Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammography, pretrained on the largest…
Feature Quality and Adaptability of Medical Foundation Models: A Comparative Evaluation for Radiographic Classification and Segmentation
Frank Li, Theo Dapamede, Mohammadreza Chavoshi +12
Foundation models (FMs) promise to generalize medical imaging, but their effectiveness varies. It remains unclear how pre-training domain (medical vs. general), paradigm (e.g., tex…
Impact of Label Noise from Large Language Models Generated Annotations on Evaluation of Diagnostic Model Performance
Mohammadreza Chavoshi, Hari Trivedi, Janice Newsome +6
Large language models (LLMs) are increasingly used to generate labels from radiology reports to enable large-scale AI evaluation. However, label noise from LLMs can introduce bias…
Evaluating Vision Language Models (VLMs) for Radiology: A Comprehensive Analysis
Frank Li, Hari Trivedi, Bardia Khosravi +8
Foundation models, trained on vast amounts of data using self-supervised techniques, have emerged as a promising frontier for advancing artificial intelligence (AI) applications in…
A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study
Jungkyu Park, Jan Witowski, Yanqi Xu +8
Although digital breast tomosynthesis (DBT) improves diagnostic performance over full-field digital mammography (FFDM), false-positive recalls remain a concern in breast cancer scr…
Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk
Theodorus Dapamede, Aisha Urooj, Vedant Joshi +15
Women are underdiagnosed and undertreated for cardiovascular disease. Automatic quantification of breast arterial calcification on screening mammography can identify women at risk…
Subgroup Performance of a Commercial Digital Breast Tomosynthesis Model for Breast Cancer Detection
Beatrice Brown-Mulry, Rohan Satya Isaac, Sang Hyup Lee +10
While research has established the potential of AI models for mammography to improve breast cancer screening outcomes, there have not been any detailed subgroup evaluations perform…
Emory Knee Radiograph (MRKR) Dataset
Brandon Price, Jason Adleberg, Kaesha Thomas +6
The Emory Knee Radiograph (MRKR) dataset is a large, demographically diverse collection of 503,261 knee radiographs from 83,011 patients, 40% of which are African American. This da…
The Limits of Fair Medical Imaging AI In The Wild
Yuzhe Yang, Haoran Zhang, Judy W Gichoya +2
As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Prior…
Hierarchical Classification System for Breast Cancer Specimen Report (HCSBC) -- an end-to-end model for characterizing severity and diagnosis
Thiago Santos, Harish Kamath, Christopher R. McAdams +8
Automated classification of cancer pathology reports can extract information from unstructured reports and categorize each report into structured diagnosis and severity categories.…
Benchmarking bias: Expanding clinical AI model card to incorporate bias reporting of social and non-social factors
Carolina A. M. Heming, Mohamed Abdalla, Shahram Mohanna +8
Clinical AI model reporting cards should be expanded to incorporate a broad bias reporting of both social and non-social factors. Non-social factors consider the role of other fact…
Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research
Bardia Khosravi, Frank Li, Theo Dapamede +8
Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, partic…
Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography
Linglin Zhang, Beatrice Brown-Mulry, Vineela Nalla +7
Although deep learning models for abnormality classification can perform well in screening mammography, the demographic, imaging, and clinical characteristics associated with incre…
A general-purpose AI assistant embedded in an open-source radiology information system
Saptarshi Purkayastha, Rohan Isaac, Sharon Anthony +4
Radiology AI models have made significant progress in near-human performance or surpassing it. However, AI model's partnership with human radiologist remains an unexplored challeng…
Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest X-Rays using Transfer Learning and Fusion Strategies
Ryan Wang, Li-Ching Chen, Lama Moukheiber +11
Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world and the third leading cause of mortality worldwide. It is often underdiagnosed…
Advances in Prediction of Readmission Rates Using Long Term Short Term Memory Networks on Healthcare Insurance Data
Shuja Khalid, Francisco Matos, Ayman Abunimer +6
30-day hospital readmission is a long standing medical problem that affects patients' morbidity and mortality and costs billions of dollars annually. Recently, machine learning mod…
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited triplets
Ananth Reddy Bhimireddy, John Lee Burns, Saptarshi Purkayastha +1
Deep learning approaches applied to medical imaging have reached near-human or better-than-human performance on many diagnostic tasks. For instance, the CheXpert competition on det…
OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System
Xiaoyuan Guo, Jiali Duan, Saptarshi Purkayastha +3
Improving the retrieval relevance on noisy datasets is an emerging need for the curation of a large-scale clean dataset in the medical domain. While existing methods can be applied…
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.5M Screening and Diagnostic Mammograms
Jiwoong J. Jeong, Brianna L. Vey, Ananth Reddy +13
Developing and validating artificial intelligence models in medical imaging requires datasets that are large, granular, and diverse. To date, the majority of publicly available bre…
MedShift: identifying shift data for medical dataset curation
Xiaoyuan Guo, Judy Wawira Gichoya, Hari Trivedi +2
To curate a high-quality dataset, identifying data variance between the internal and external sources is a fundamental and crucial step. However, methods to detect shift or varianc…
Two-step adversarial debiasing with partial learning -- medical image case-studies
Ramon Correa, Jiwoong Jason Jeong, Bhavik Patel +3
The use of artificial intelligence (AI) in healthcare has become a very active research area in the last few years. While significant progress has been made in image classification…
CVAD: A generic medical anomaly detector based on Cascade VAE
Xiaoyuan Guo, Judy Wawira Gichoya, Saptarshi Purkayastha +1
Detecting out-of-distribution (OOD) samples in medical imaging plays an important role for downstream medical diagnosis. However, existing OOD detectors are demonstrated on natural…
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns +20
Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obviou…
A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals
Ju Sun, Le Peng, Taihui Li +14
Importance: An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate cli…