tblcswsss
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: SupCodeTech
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 42.2 MB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 5
- Releases: 0
Metadata Files
README.md
Note: As the paper is still in the REVIEW stage, the project code has not yet been fully uploaded. This project is still under construction
The code is based on MONAI (AI Toolkit for Healthcare Imaging).
Env configuration
bash
pip install monai
conda create -n unest python=3.7
conda activate unest
pip install monai nibabel
pip install -r requirements.txt
Datasets
The dataset is available in the U.S. National Institute of Allergy & Infectious Diseases (NIH) TB portals dataset and DEEPPulmtb dataset.
Please look at the Data Description below for detailed information about the dataset.
After decompressing the dataset, you can get the following directory:
none
├── Train_data
│ ├── Training_Dataset
│ │ ├── TRN_00.nii.gz
│ │ ├── TRN_000.nii.gz
│ │ ├── …
Training Data (ImageCLEF training data) Preparation
We need to download the following datasets:
ImageCLEF 2022 Tuberculosis - Caverns Report and Caverns Detection
and unzip the Cavern Detection Train CT files (1 to 7) and the Cavern Report Train CT files (1 to 2):
```none
| Zip files Unzip files |
| de0e8772-594d-41ce-9e85-578c4b59e9f3detectiontrainCT1 (TRN000 - TRN099) | | 29cc320d-1c9d-4c7c-8d12-a006499c2f2fdetectiontrainCT2 (TRN100 - TRN179) | | 3b9da027-6015-4806-bbe9-04efb760ee53detectiontrainCT3 (TRN180 - TRN269) | | c45b6a20-e3dc-49b5-bd93-1a86dc10721cdetectiontrainCT4 (TRN270 - TRN339) | | 447557ed-45dd-4468-8381-dbf0642b4312detectiontrainCT5 (TRN340 - TRN419) | | f9da57c9-dbb5-4a3d-bebb-0618a8aef99fdetectiontrainCT6 (TRN420 - TRN499) |
| eac9b86a-7673-4b24-924a-29529de6f130detectiontrainCT7 (TRN500 - TRN558) |
| 45037ba5-e1e7-4011-98c6-35ed190e204acavernreporttrainCT1 (TRN00 - TRN_29) |
| e222bfea-28db-4d3a-b7cf-b68a7e18992bcavernreporttrainCT2 (TRN30 - TRN_59) |
``` they will be placed in the following directory:
```none ├── Traindata │ ├── OriginalImageDataset │ │ ├── TRN00.nii.gz │ │ ├── TRN_000.nii.gz │ │ ├── …
```
Training Process
For supervised paradigm training
bash
python main.py
For weakly supervised paradigm training
Run the example script:
sh
sh scripts/train_r50_SwinUNeLCsXt.sh
Individual module training
For Symmetrized Graph Convolutional Semantic Affinity
bash
python train/SwinUNeLCsT_SGCSA_Module.py
For Class-driven Affinity Pseudo Label Generation
bash
python train/SwinUNeLCsT_CLS_CAM.py
Affinity Pixel-Level Pseudo Refinement Adjustment
bash
python train/SwinUNeLCsT_APLPRA.py
Basic Supervised Semantic Segmentation
bash
python train/SwinUNeLCsT_basic_seg.py
End-to-end integration training
bash
python train/Endtoend_inter_training.py
Note: As the paper is still in the REVIEW stage, the project code has not yet been fully uploaded. This project is still under construction
Contact
If you have any questions, please feel free to contact me via tan.joey@pelajar.upm.edu.my
LICENSE
This repo is under the Apache-2.0 license. For commercial use, please contact the authors.
Owner
- Login: SupCodeTech
- Kind: user
- Location: Malaysia
- Company: UPM
- Repositories: 2
- Profile: https://github.com/SupCodeTech
Ph.D. Student at UPM
Citation (CITATION.cff)
# YAML 1.2
# Metadata for citation of this software according to the CFF format (https://citation-file-format.github.io/)
#
---
title: "MONAI: Medical Open Network for AI"
abstract: "AI Toolkit for Healthcare Imaging"
authors:
- name: "MONAI Consortium"
date-released: 2023-06-08
version: "1.2.0"
identifiers:
- description: "This DOI represents all versions of MONAI, and will always resolve to the latest one."
type: doi
value: "10.5281/zenodo.4323058"
license: "Apache-2.0"
repository-code: "https://github.com/Project-MONAI/MONAI"
url: "https://monai.io"
cff-version: "1.2.0"
message: "If you use this software, please cite it using these metadata."
preferred-citation:
type: article
authors:
- given-names: "M. Jorge"
family-names: "Cardoso"
- given-names: "Wenqi"
family-names: "Li"
- given-names: "Richard"
family-names: "Brown"
- given-names: "Nic"
family-names: "Ma"
- given-names: "Eric"
family-names: "Kerfoot"
- given-names: "Yiheng"
family-names: "Wang"
- given-names: "Benjamin"
family-names: "Murray"
- given-names: "Andriy"
family-names: "Myronenko"
- given-names: "Can"
family-names: "Zhao"
- given-names: "Dong"
family-names: "Yang"
- given-names: "Vishwesh"
family-names: "Nath"
- given-names: "Yufan"
family-names: "He"
- given-names: "Ziyue"
family-names: "Xu"
- given-names: "Ali"
family-names: "Hatamizadeh"
- given-names: "Wentao"
family-names: "Zhu"
- given-names: "Yun"
family-names: "Liu"
- given-names: "Mingxin"
family-names: "Zheng"
- given-names: "Yucheng"
family-names: "Tang"
- given-names: "Isaac"
family-names: "Yang"
- given-names: "Michael"
family-names: "Zephyr"
- given-names: "Behrooz"
family-names: "Hashemian"
- given-names: "Sachidanand"
family-names: "Alle"
- given-names: "Mohammad"
family-names: "Zalbagi Darestani"
- given-names: "Charlie"
family-names: "Budd"
- given-names: "Marc"
family-names: "Modat"
- given-names: "Tom"
family-names: "Vercauteren"
- given-names: "Guotai"
family-names: "Wang"
- given-names: "Yiwen"
family-names: "Li"
- given-names: "Yipeng"
family-names: "Hu"
- given-names: "Yunguan"
family-names: "Fu"
- given-names: "Benjamin"
family-names: "Gorman"
- given-names: "Hans"
family-names: "Johnson"
- given-names: "Brad"
family-names: "Genereaux"
- given-names: "Barbaros S."
family-names: "Erdal"
- given-names: "Vikash"
family-names: "Gupta"
- given-names: "Andres"
family-names: "Diaz-Pinto"
- given-names: "Andre"
family-names: "Dourson"
- given-names: "Lena"
family-names: "Maier-Hein"
- given-names: "Paul F."
family-names: "Jaeger"
- given-names: "Michael"
family-names: "Baumgartner"
- given-names: "Jayashree"
family-names: "Kalpathy-Cramer"
- given-names: "Mona"
family-names: "Flores"
- given-names: "Justin"
family-names: "Kirby"
- given-names: "Lee A.D."
family-names: "Cooper"
- given-names: "Holger R."
family-names: "Roth"
- given-names: "Daguang"
family-names: "Xu"
- given-names: "David"
family-names: "Bericat"
- given-names: "Ralf"
family-names: "Floca"
- given-names: "S. Kevin"
family-names: "Zhou"
- given-names: "Haris"
family-names: "Shuaib"
- given-names: "Keyvan"
family-names: "Farahani"
- given-names: "Klaus H."
family-names: "Maier-Hein"
- given-names: "Stephen"
family-names: "Aylward"
- given-names: "Prerna"
family-names: "Dogra"
- given-names: "Sebastien"
family-names: "Ourselin"
- given-names: "Andrew"
family-names: "Feng"
doi: "https://doi.org/10.48550/arXiv.2211.02701"
month: 11
year: 2022
title: "MONAI: An open-source framework for deep learning in healthcare"
...
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