Science Score: 44.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: Thanaporn09
  • Language: Python
  • Default Branch: main
  • Size: 10.6 MB
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  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
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Created about 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme Contributing Code of conduct Citation

README.md

GeoRefineNet: A Multistage Framework for Enhanced Cephalometric Landmark Detection in CBCT Images Using 3D Geometric Information

This is the official PyTorch implementation repository of our GeoRefineNet: A Multistage Framework for Enhanced Cephalometric Landmark Detection in CBCT Images Using 3D Geometric Information <- Link to paper: >

Prerequisites

  • Python 3.7
  • MMpose 0.23
  • pyronn (PyTorch version): (https://github.com/theHamsta/pyronn-torch)

Usage of the code

  • Dataset preparation
    • The dataset structure should be in the following structure:

Inputs: PNG images and JSON file └── <dataset name> ├── 2D_images | ├── 001.png │ ├── 002.png │ ├── 003.png │ ├── ... | └── JSON ├── train.json └── test.json - Example json format { "images": [ { "id": 0, "file_name": "0.png", "height": 420, "width": 620 }, ... ], "annotations": [ { "image_id": 0, "id": 0, "category_id": 1, "keypoints": [ 604.5070198755171, 289.1590783982888, 2, 592.8121081534473, 261.62600827462876, 2, 428.0154934462112, 301.24809471563935, 2, 604.9223114040644, 234.45993184950234, 2, 570.296873380625, 182.90429052972533, 2, 456.97751121306436, 208.8105499707776, 2, 369.95414168150415, 239.07609878665616, 2, 307.83364934785106, 229.91052362204155, 2, 373.5995213621739, 353.599939601835, 2, 499.50552505239256, 453.1111418891231, 2, 493.50543334239256, 456.12341418891231, 2 ], "num_keypoints": 11, "iscrowd": 0 }, ... ]

  • Output: 2D landmark coordinates

Owner

  • Login: Thanaporn09
  • Kind: user

Citation (CITATION.cff)

message: "(Citaion will be upated) If you use this code, please cite it as below."
authors:
  - name: "Thanaporn Viriyasaranon, Serie Ma, and Jang-Hwan Choi"
title: "Anatomical Landmark Detection Using a Multiresolution Learning Approach with a Hybrid Transformer-CNN Model"
date-released: 2020-05-30
url: "https://github.com/seriee/Multiresolution-Learning-based-Hybrid-Transformer-CNN-Model-for-Anatomical-Landmark-Detection"

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.github/workflows/deploy.yml actions
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.github/workflows/lint.yml actions
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docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
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requirements.txt pypi
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