1 citations · 1 across the 3 of their papers we have counts for
4 papers
MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging
Leonard Nürnberg, Dennis Bontempi, Suraj Pai +17
Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are…
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
Gowtham Krishnan Murugesan, Diana McCrumb, Rahul Soni +8
AI in Medical Imaging project aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by developing nnU-Net models and providing AI-assisted segmentations fo…
Improving Lesion Segmentation in FDG-18 Whole-Body PET/CT scans using Multilabel approach: AutoPET II challenge
Gowtham Krishnan Murugesan, Diana McCrumb, Eric Brunner +5
Automatic segmentation of lesions in FDG-18 Whole Body (WB) PET/CT scans using deep learning models is instrumental for determining treatment response, optimizing dosimetry, and ad…
AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons
Gowtham Krishnan Murugesan, Diana McCrumb, Mariam Aboian +10
The National Cancer Institute (NCI) Image Data Commons (IDC) offers publicly available cancer radiology collections for cloud computing, crucial for developing advanced imaging too…