HistoSeg
importedsoftware/histoseg
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…
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
- Homepage
- unknown
- Repository
- github.com/saadwazir/HistoSeg
- Documentation
- unknown
- Tags
- attention-mechanism · deeplab-v3-plus · dice-loss · focal-loss · glas · histological-image-segmentation · histological-images · histology-images
- Regulatory
- unknown
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Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
State-of-the-Art Pneumonia detection from chest X-rays system using EfficientNetV2 + FPN + Faster R-CNN. Features Focal Loss, Weighted Box Fusion, Mosaic Augmentation & StratifiedGroupKFold. Built…
- OEEMhistological-images
MICCAI 2022: Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images
- PanopMambahistological-image-segmentation
Official implementation of "PanopMamba: Vision State Space Modeling for Nuclei Panoptic Segmentation".
- MRH-Nethistology-images
Training a local neural network from multiple MRI to histology
- PyHistologyhistology-images
Python package that uses colorspace-based segmentation to analyze histopathology images.
- MCTSegdice-loss
[Preprint] Official implementation of "A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities".
- api.github.com/repos/saadwazir/HistoSegretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2025-04-11, 26 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/59.json→ .entries["histoseg"]
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