openmedical/registry
← registry

SE-Attention-Half-UNet

imported

software/se-attention-half-unet

Implementation of a compact Attention Half U-Net with Attention Gates and Squeeze-and-Excitation blocks for medical image segmentation. Features a modular PyTorch pipeline, BCE-Dice hybrid loss,…

Machine-generated from the listed sources and not yet reviewed by a human.

SE-Attention-Half-UNet project image
GitHub preview card for ChaitanyaParate/SE-Attention-Half-UNet. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
active
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
attention-gate · binary-segmentation · computer-vision · deep-learning · dice-loss · medical-image-segmentation · pytorch · squeeze-excitation · unet-pytorch
Regulatory
unknown
similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • Lung-Tumor-Segmentation-DSmedical-image-segmentation · pytorch · unet-pytorch

    Lung tumor segmentation with the UNet model.

  • MCTSegdice-loss · medical-image-segmentation

    [Preprint] Official implementation of "A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities".

  • A 3D Attention U-Net model is developed, aimed at segmenting and tracking Multiple Sclerosis lesions in MRI images.

  • Mobile-U-ViTmedical-image-segmentation · unet-pytorch

    [ACM MM 2025] Mobile U-ViT: Revisiting large kernel and U-shaped ViT for efficient medical image segmentation

  • spectral-mamba-analysiscomputer-vision · medical-image-segmentation · pytorch

    Comparative spectral analysis of CNNs, Transformers, and State Space Models for medical image segmentation. Introduces AVR-based spectral fingerprinting across architecture paradigms.

  • HistoSegdice-loss

    HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy. This…

sources
  1. api.github.com/repos/ChaitanyaParate/SE-Attention-Half-UNet
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2026-06-11, 3 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 →

machine-readable

/v1/entries/1.json→ .entries["se-attention-half-unet"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.