nitrain
importedsoftware/nitrain
Train AI models efficiently on medical images using any framework
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- AGPL-3.0(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- nitrain
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/nitrain/nitrain
- Documentation
- unknown
- Tags
- deep-learning · keras · medical-imaging · neuroimaging · pytorch
- 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.
- Brain-tumor-classifierkeras · medical-imaging · neuroimaging
Brain tumor classification model from MRI scans using a Convolutional Neural Newtwork (CNN) built with Tensor flow/Keras.
- clinicadlmedical-imaging · neuroimaging · pytorch
Open-source Python library for reproducible deep learning in neuroimaging
- mindGlidemedical-imaging · neuroimaging · pytorch
Brain MRI segmentation for multiple sclerosis — any sequence, any quality. pip install mindglide
- niftiaimedical-imaging · neuroimaging · pytorch
Train neural nets on 3D images (e.g. MRIs) 🧠
- AMBERkeras · pytorch
Automated Modelling for Biological Evidence-based Research
- ProjectAiAikeras · pytorch
AiAi.care project is teaching computers to "see" chest X-rays and interpret them how a human Radiologist would. We are using 700,000 Chest X-Rays + Deep Learning to build an FDA 💊 approved,…
- api.github.com/repos/nitrain/nitrainretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2024-06-13, 1880 stars, license reported as AGPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/42.json→ .entries["nitrain"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.