UNetTransplant
importedsoftware/unettransplant
Repository for the paper "U-Net Transplant: The Role of Pre-training for Model Merging in 3D Medical Segmentation" accepted @ MICCAI2025
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- Apache-2.0(osi)
- Status
- maintained
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Repository
- github.com/LucaLumetti/UNetTransplant
- Documentation
- unknown
- Tags
- 3d-segmentation · computer-vision · deep-learning · medical-imaging · miccai2025 · model-merging
- Regulatory
- unknown
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- Brain-Tumors-Segmentation3d-segmentation · computer-vision · medical-imaging
Multimodal Brain mpMRI segmentation on BraTS 2023 and BraTS 2021 datasets.
- brainchop3d-segmentation · medical-imaging
Brainchop: In-browser 3D MRI rendering and segmentation
- SAAMI3d-segmentation · medical-imaging
Automatic segment and generate masks for any 3D medical images using SAM model without prompt
- U-Net-PyTorch3d-segmentation · medical-imaging
🧠 Implement U-Net in PyTorch for effective binary image segmentation, focusing on brain tumor detection with a complete pipeline from data prep to evaluation.
- 3D-Volumetric-MedicalImageSegmentationWithDeepLearning3d-segmentation
This GitHub repository was created for research focusing on the development of deep learning-based segmentation models for fetal brain tissue.
- Fast-nnUNet3d-segmentation
This is the official repository for Fast-nnUNet, a new fast model inference framework based on the nnUNet framework implementation.
- api.github.com/repos/LucaLumetti/UNetTransplantretrieved 2026-08-25 · via github-api
Machine-imported from GitHub search. Last push 2025-06-26, 31 stars, license reported as Apache-2.0. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/60.json→ .entries["unettransplant"]
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