568 citations · 614 across the 13 of their papers we have counts for
13 papers
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…
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…
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…
PathologyBERT -- Pre-trained Vs. A New Transformer Language Model for Pathology Domain
Thiago Santos, Amara Tariq, Susmita Das +4
Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions. However, successful text mining of a large pat…
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…
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…
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications
Imon Banerjee, Priyanshu Sinha, Saptarshi Purkayastha +5
Purpose: Since the recent COVID-19 outbreak, there has been an avalanche of research papers applying deep learning based image processing to chest radiographs for detection of the…
A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images
Pradeeban Kathiravelu, Puneet Sharma, Ashish Sharma +7
Executing machine learning (ML) pipelines in real-time on radiology images is hard due to the limited computing resources in clinical environments and the lack of efficient data tr…