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Found 13 papers, 1 papers with code
Benchmarking bias: Expanding clinical AI model card to incorporate bias reporting of social and non-social factors
Clinical AI model reporting cards should be expanded to incorporate a broad bias reporting of both social and non-social factors.
Hierarchical Classification System for Breast Cancer Specimen Report (HCSBC) -- an end-to-end model for characterizing severity and diagnosis
Automated classification of cancer pathology reports can extract information from unstructured reports and categorize each report into structured diagnosis and severity categories.
Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography
However, after controlling for confounding, we found lower FN risk associates with Other race(RR=0. 828;p=. 050), biopsy-proven benign lesions(RR=0. 927;p=. 011), and mass(RR=0. 921;p=. 010) or asymmetry(RR=0. 854;p=. 040); higher FN risk associates with architectural distortion (RR=1. 037;p<. 001).
Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest X-Rays using Transfer Learning and Fusion Strategies
We further propose two fusion schemes, (1) model-level fusion, including bagging and stacking methods using MIMIC-CXR, and (2) data-level fusion, including multi-site data using MIMIC-CXR and Emory-CXR, and multi-modal using MIMIC-CXRs and MIMIC-IV EHR, to improve the overall model performance.
Advances in Prediction of Readmission Rates Using Long Term Short Term Memory Networks on Healthcare Insurance Data
We developed a bi-directional Long Short Term Memory (LSTM) Network that is able to use readily available insurance data (inpatient visits, outpatient visits, and drug prescriptions) to predict 30 day re-admission for any admitted patient, regardless of reason.
PathologyBERT -- Pre-trained Vs. A New Transformer Language Model for Pathology Domain
Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions.
OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System
While existing methods can be applied for class-wise retrieval (aka.
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.5M Screening and Diagnostic Mammograms
Developing and validating artificial intelligence models in medical imaging requires datasets that are large, granular, and diverse.
MedShift: identifying shift data for medical dataset curation
Given an internal dataset A as the base source, we first train anomaly detectors for each class of dataset A to learn internal distributions in an unsupervised way.
Two-step adversarial debiasing with partial learning -- medical image case-studies
The use of artificial intelligence (AI) in healthcare has become a very active research area in the last few years.
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications
To test the performance of the two top models for CXR COVID-19 diagnosis on external datasets to assess model generalizability.
A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images
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 transfer capabilities to run them on research clusters.
