AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons
Abstract The Artificial Intelligence in Medical Imaging (AIMI) initiative aims to enhance the National Cancer Institute’s (NCI) Image Data Commons (IDC) by releasing fully reproducible nnU-Net models, along with AI-assisted segmentation for cancer radiology images. In this extension of our earlier w...
Saved in:
| Main Authors: | Gowtham Krishnan Murugesan, Diana McCrumb, Rahul Soni, Jithendra Kumar, Leonard Nuernberg, Linmin Pei, Ulrike Wagner, Sutton Granger, Andrey Y. Fedorov, Stephen Moore, Jeff Van Oss |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05666-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Analysis of Connectivity in Electromyography Signals to Examine Neural Correlations in the Activation of Lower Leg Muscles for Postural Stability: A Pilot Study
by: Gordon Alderink, et al.
Published: (2025-01-01) -
Comprehensive functional annotation of 77 prostate cancer risk loci.
by: Dennis J Hazelett, et al.
Published: (2014-01-01) -
Effective Tumor Annotation for Automated Diagnosis of Liver Cancer
by: Yi-Hsuan Chuang, et al.
Published: (2025-01-01) -
CaGe: A Web-Based Cancer Gene Annotation System for Cancer Genomics
by: Young-Kyu Park, et al.
Published: (2012-03-01) -
Functional annotation of the Hippo pathway somatic mutations in human cancers
by: Han Han, et al.
Published: (2024-11-01)