1 citations · 1 across the 6 of their papers we have counts for
6 papers
Knowledge-based anomaly detection for identifying network-induced shape artifacts
Rucha Deshpande, Tahsin Rahman, Miguel Lago +6
Synthetic data provides a promising approach to address data scarcity for training machine learning models; however, adoption without proper quality assessments may introduce artif…
HistoART: Histopathology Artifact Detection and Reporting Tool
Seyed Kahaki, Alexander R. Webber, Ghada Zamzmi +3
In modern cancer diagnostics, Whole Slide Imaging (WSI) is widely used to digitize tissue specimens for detailed, high-resolution examination; however, other diagnostic approaches,…
Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features
Miguel A. Lago, Ghada Zamzmi, Brandon Eich +1
Explainability features are intended to provide insight into the internal mechanisms of an AI device, but there is a lack of evaluation techniques for assessing the quality of prov…
Synthetic Data in Radiological Imaging: Current State and Future Outlook
Elena Sizikova, Andreu Badal, Jana G. Delfino +6
A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and repre…
Scorecards for Synthetic Medical Data Evaluation and Reporting
Ghada Zamzmi, Adarsh Subbaswamy, Elena Sizikova +3
Although interest in synthetic medical data (SMD) for training and testing AI methods is growing, the absence of a standardized framework to evaluate its quality and applicability…
Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control
Ghada Zamzmi, Kesavan Venkatesh, Brandon Nelson +4
Background: Machine learning (ML) methods often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices in clinical sett…