Search Results for author:
Found 13 papers, 4 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.
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).
Report of the Medical Image De-Identification (MIDI) Task Group -- Best Practices and Recommendations
Only technical issues of public sharing are addressed.
Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics
Language modality within the vision language pretraining framework is innately discretized, endowing each word in the language vocabulary a semantic meaning.
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited triplets
We compare our retrained model performance with existing FSL approaches in medical imaging that train and evaluate models at once.
OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System
While existing methods can be applied for class-wise retrieval (aka.
Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification
The goal of this series is to provide resources to not only help improve the review process for A. I.-based medical imaging papers, but to facilitate a standard for the information that is presented within all components of the research study.
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.
CVAD: A generic medical anomaly detector based on Cascade VAE
The key issue is the granularity of OOD data in the medical domain, where intra-class OOD samples are predominant.
Margin-Aware Intra-Class Novelty Identification for Medical Images
Traditional anomaly detection methods focus on detecting inter-class variations while medical image novelty identification is inherently an intra-class detection problem.
A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals
Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct, but not replacement, for clinical decision support of COVID-19 diagnosis, which largely hinges on exposure history, signs, and symptoms.
Generalization of Deep Convolutional Neural Networks -- A Case-study on Open-source Chest Radiographs
The results show the internal performance of each of the 5 pathologies outperformed external performance on both of the models.
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.
