contextual-relationships-for-cervical-cell-detection
https://github.com/fengshuo96/contextual-relationships-for-cervical-cell-detection
Science Score: 44.0%
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Low similarity (7.8%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: FengShuo96
- License: apache-2.0
- Language: Python
- Default Branch: master
- Size: 37 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
Contextual Relationships for Cervical Cell Detection
This is the code implementation of the RoI attention method. Our code is built on the basis of MMDetection.
MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project developed by Multimedia Laboratory, CUHK.
The master branch works with PyTorch 1.6 and MMDetection v2.6.0.
Datasets
We collected a liquid-based cervical cytology images dataset, called Cervical Cell Detection (CCD) dataset. The CCD dataset consists of 40,000 pathological images, 30,000 training and 10,000 testing. All annotated instances belong to 10 categories i.e., negative for intraepithelial lesion for malignancy (NILM), atypical squamous cells-undetermined significance (ASCUS), atypical squamous cells-cannot exclude HSIL (ASCH), low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), atypical glandular cells (AGC), vaginalis trichomoniasis (VAG), monilia (MON), dysbacteriosis (DYS) and endocervical cells (EC), etc.
Method
The implementation of the GRAM and RRAM is in roiattentionhead.py.
Conifgs
We set up 3 config files to realize GRAM and RRAM including different combination strategies. Refer to configs/roi_annention for details.
Main Results
Train log of Cascade RRAM and GRAM is in 20211214_100303.log.json. The trained model is available on google driver here.
Model | mAP@50 | mAP@75 | mAP --- |:---:|:---:|:---: Faster R-CNN with FPN (baseline) | 50.3 | 29.5 | 28.6 RRAM | 52.7 | 31.2 | 30.1 GRAM | 52.8 | 30.9 | 30.0 Cascade RRAM and GRAM | 53.3 | 31.5 | 30.4
Contact
This repo is currently maintained by Shuo Feng (@FengShuo96).
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - name: "MMDetection Contributors" title: "OpenMMLab Detection Toolbox and Benchmark" date-released: 2018-08-22 url: "https://github.com/open-mmlab/mmdetection" license: Apache-2.0
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Dependencies
- albumentations >=0.3.2
- cython *
- numpy *
- docutils ==0.16.0
- myst-parser *
- sphinx ==4.0.2
- sphinx-copybutton *
- sphinx_markdown_tables *
- sphinx_rtd_theme ==0.5.2
- mmcv-full >=1.3.17
- cityscapesscripts *
- imagecorruptions *
- scipy *
- sklearn *
- timm *
- mmcv *
- torch *
- torchvision *
- matplotlib *
- numpy *
- pycocotools *
- six *
- terminaltables *
- asynctest * test
- codecov * test
- flake8 * test
- interrogate * test
- isort ==4.3.21 test
- kwarray * test
- onnx ==1.7.0 test
- onnxruntime >=1.8.0 test
- protobuf <=3.20.1 test
- pytest * test
- ubelt * test
- xdoctest >=0.10.0 test
- yapf * test