7 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.LG2024
ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics
Oishi Banerjee, Agustina Saenz, Kay Wu +17
Given the rapidly expanding capabilities of generative AI models for radiology, there is a need for robust metrics that can accurately measure the quality of AI-generated radiology…
eess.IV2022★ 7 cited
AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier
Lars Heiliger, Zdravko Marinov, Max Hasin +9
Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy. In this context, FDG-PET/CT scans are routinely used for staging and re-sta…