most citedAutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier

7 citations · 7 across the 2 of their papers we have counts for

collaborators

2 papers

eess.IV2026

GazeXPErT: An Expert Eye-tracking Dataset for Interpretable and Explainable AI in Oncologic FDG-PET/CT Scans

Joy T Wu, Daniel Beckmann, Sarah Miller +15

[18F]FDG-PET/CT is a cornerstone imaging modality for guiding oncology therapies, yet human expert shortages necessitate more efficient diagnostic aids. While standalone AI models…

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…