iCVMapp3r
importedsoftware/icvmapp3r
AICONSlab's brain extraction (skull-stripping) algorithm using CNNs
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- GPL-3.0(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- AICONSlab
- Country
- unknown
- Homepage
- icvmapp3r.readthedocs.io
- Repository
- github.com/AICONSlab/iCVMapp3r
- Documentation
- unknown
- Tags
- brain-segmentation · cnn · deep-learning · image-processing · medical-imaging · mri · neuroimaging · neuroscience
- 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.
- VentMapp3rbrain-segmentation · cnn · medical-imaging · mri
AICONSlab's ventricular segmentation technique using CNNs
- HippMapp3rcnn · image-processing · medical-imaging · mri
AICONSlab's hippocampal segmentation algorithm using CNNs
- babysegbrain-segmentation · mri · neuroimaging
Brain segmentation across the first years of life
- Brain-tumor-classifiercnn · medical-imaging · mri · neuroimaging
Brain tumor classification model from MRI scans using a Convolutional Neural Newtwork (CNN) built with Tensor flow/Keras.
- n4aximage-processing · medical-imaging · mri · neuroimaging
JAX/GPU N4 bias field correction — a fast drop-in match for SimpleITK N4 (~2600x faster on A100, <0.2% match)
- HyperDenseNetbrain-segmentation · cnn
This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.
- api.github.com/repos/AICONSlab/iCVMapp3rretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2024-10-08, 18 stars, license reported as GPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.
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/v1/entries/50.json→ .entries["icvmapp3r"]
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