t-loss
importedsoftware/t-loss
Official code for Robust T-Loss for Medical Image Segmentation (MICCAI 2023)
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- Apache-2.0(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- Digital-Dermatology
- Country
- unknown
- Homepage
- robust-tloss.github.io/
- Repository
- github.com/Digital-Dermatology/t-loss
- Documentation
- unknown
- Tags
- image-segmentation · medical-image-analysis · medical-imaging · miccai2023 · pytorch · robust-segmentation · t-loss · tloss
- 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.
- federated_heimage-segmentation · medical-image-analysis · medical-imaging · pytorch
Federated learning with homomorphic encryption enables multiple parties to securely co-train artificial intelligence models in pathology and radiology, reaching state-of-the-art performance with…
- DHCmiccai2023
MICCAI 2023: DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation
- angiodysplasia-segmentationimage-segmentation · medical-imaging · pytorch
Wining solution and its further development for MICCAI 2017 Endoscopic Vision Challenge Angiodysplasia Detection and Localization
- BTSC-UNet-ViTimage-segmentation · medical-imaging · pytorch
🧠 BTSC-UNet-ViT— a🚀brain tumor segmentation & classification system combining 🧩Vision Transformers (ViT) for tumor detection and 🎯 UNet for precise pixel-level segmentation from MRI scans.…
- MISTimage-segmentation · medical-imaging · pytorch
MIST: A simple and scalable end-to-end framework for 3D medical imaging segmentation.
- QG-SSLimage-segmentation · medical-imaging · pytorch
[MICCAI '26 Early Accept] Quality-Guided Semi-Supervised Learning for Medical Image Segmentation
- api.github.com/repos/Digital-Dermatology/t-lossretrieved 2026-08-25 · via github-api
Machine-imported from GitHub search. Last push 2023-10-29, 61 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/61.json→ .entries["t-loss"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.