45 citations · 125 across the 12 of their papers we have counts for
12 papers
Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations
Deepa Krishnaswamy, Dennis Bontempi, Vamsi Thiriveedhi +6
Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived featur…
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Vikash Gupta, Barbaros Selnur Erdal, Carolina Ramirez +24
Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload o…
Improving the repeatability of deep learning models with Monte Carlo dropout
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge +7
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Repeatability is a key attribute of model robustness. Repeatable models outp…
Monte Carlo dropout increases model repeatability
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge +5
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Among the main features of robustness is repeatability. Much attention is gi…
Deploying clinical machine learning? Consider the following...
Charles Lu, Ken Chang, Praveer Singh +5
Despite the intense attention and considerable investment into clinical machine learning research, relatively few applications have been deployed at a large-scale in a real-world c…
Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology
Christopher P. Bridge, Chris Gorman, Steven Pieper +6
Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperabil…