BOA-Contrast
importedsoftware/boa-contrast
Computes CT contrast phase and GI tract contrast using TotalSegmentator and ML
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- active
- Maturity
- deployed
- Organization
- UMEssen
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/UMEssen/BOA-Contrast
- Documentation
- unknown
- Tags
- contrast-phase-classification · contrast-recognition · ct-imaging · gastrointestinal-tract · image-segmentation · intravenous-contrast · medical-imaging · ml-inference
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- ACC-UNetimage-segmentation · medical-imaging
ACC-UNet is A Completely Convolutional UNet model inspired from transformer-based UNets
- HippMapp3rimage-segmentation · medical-imaging
AICONSlab's hippocampal segmentation algorithm using CNNs
- HyperMapp3rimage-segmentation · medical-imaging
AICONSlab's white matter hyperintensities (WMH) segmentation algorithm using CNNs
- MedSegDiffimage-segmentation · medical-imaging
Using Diffusion Models to Segment/Reconstruct Organs from Medical Images [AAAI Most influential Paper]
- tcga_segmentationimage-segmentation · medical-imaging
Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024
- u-netimage-segmentation · medical-imaging
U-Net: Convolutional Networks for Biomedical Image Segmentation
- api.github.com/repos/UMEssen/BOA-Contrastretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2026-03-11, 9 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
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/v1/entries/2.json→ .entries["boa-contrast"]
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