CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AI

1 code implementation • 6 Feb 2022 • Arjun Soin, Jameson Merkow, Jin Long, Joseph Paul Cohen, Smitha Saligrama, Stephen Kaiser, Steven Borg, Ivan Tarapov, Matthew P Lungren

We use the CheXpert and PadChest public datasets to build and test a medical imaging AI drift monitoring workflow to track data and model drift without contemporaneous ground truth.

RapidRead: Global Deployment of State-of-the-art Radiology AI for a Large Veterinary Teleradiology Practice

no code implementations • 9 Nov 2021 • Michael Fitzke, Conrad Stack, Andre Dourson, Rodrigo M. B. Santana, Diane Wilson, Lisa Ziemer, Arjun Soin, Matthew P. Lungren, Paul Fisher, Mark Parkinson

This work describes the development and real-world deployment of a deep learning-based AI system for evaluating canine and feline radiographs across a broad range of findings and abnormalities.

OncoNet: Weakly Supervised Siamese Network to automate cancer treatment response assessment between longitudinal FDG PET/CT examinations

no code implementations • 3 Aug 2021 • Anirudh Joshi, Sabri Eyuboglu, Shih-Cheng Huang, Jared Dunnmon, Arjun Soin, Guido Davidzon, Akshay Chaudhari, Matthew P Lungren

FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy.

BIG-bench Machine Learning