GeoLS
importedsoftware/geols
Adding Image-context in the Label Smoothing process via Geodesic distance
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- maintained
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/adigasu/GeoLS
- Documentation
- unknown
- Tags
- geodesic · geodesic-distances · image-intensity · image-segmentation · label-smoothing · medical-image-analysis · medical-image-segmentation · segmentation
- Regulatory
- unknown
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- MHA-UNetimage-segmentation · medical-image-analysis · medical-image-segmentation
[BIBM 2025] The official code for "Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification".
- federated_heimage-segmentation · medical-image-analysis · segmentation
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…
- DAEFormermedical-image-analysis · medical-image-segmentation · segmentation
[MICCAI 2023] DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation
- t-lossimage-segmentation · medical-image-analysis
Official code for Robust T-Loss for Medical Image Segmentation (MICCAI 2023)
- AgileFormerimage-segmentation · medical-image-segmentation
This the repo for the paper tiltled "AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation"
- mmsegmentationimage-segmentation · medical-image-segmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
- api.github.com/repos/adigasu/GeoLSretrieved 2026-08-25 · via github-api
Machine-imported from GitHub search. Last push 2025-04-23, 7 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/27.json→ .entries["geols"]
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