Data

Tools

Indexes

Applications

Experiments

Open Source Science

mp3d

Code of paper: Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection on CT Slices

https://github.com/urmagicsmine/mp3d

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.5%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Code of paper: Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection on CT Slices

Basic Info
  • Host: GitHub
  • Owner: urmagicsmine
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 36.8 MB
Statistics
  • Stars: 8
  • Watchers: 1
  • Forks: 1
  • Open Issues: 3
  • Releases: 0
Created over 3 years ago · Last pushed almost 3 years ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection on CT Slices

This is an implementation of MICCAI 2020 paper Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection on CT Slices.

Installation

This code is based on MMDetection. Please see it for installation.

Data preparation

Download DeepLesion dataset here.

We provide coco-style json annotation files converted from DeepLesion. Please download json files here, unzip Images_png.zip and make sure to put files as following sturcture:

data ├──DeepLesion ├── annotations │ ├── deeplesion_train.json │ ├── deeplesion_test.json │ ├── deeplesion_val.json └── Images_png └── Images_png │ ├── 000001_01_01 │ ├── 000001_03_01 │ ├── ...

Pre-trained Model

We provide models pre-trained on COCO dataset which can be used for different 3D medical image detection.

The pre-trained MP3D63 model can be downloaded from BaiduYun(verification code: bbrc) or GoogleDrive.

Training

To train MP3D & P3d model on deeplesion dataset, run:

bash tools/dist_train.sh configs/deeplesion/mp3d_groupconv.py 8 bash tools/dist_train.sh configs/deeplesion/p3d.py 8

Contact

If you have questions or suggestions, please open an issue here.

Owner

  • Login: urmagicsmine
  • Kind: user

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

GitHub Events

Total
  • Issues event: 2
  • Watch event: 1
  • Issue comment event: 2
  • Fork event: 1
Last Year
  • Issues event: 2
  • Watch event: 1
  • Issue comment event: 2
  • Fork event: 1

Dependencies

requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/build.txt pypi
  • cython *
  • numpy *
requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
  • sphinx_rtd_theme ==0.5.2
requirements/mminstall.txt pypi
  • mmcv-full >=1.3.17
requirements/optional.txt pypi
  • cityscapesscripts *
  • imagecorruptions *
  • scipy *
  • sklearn *
  • timm *
requirements/readthedocs.txt pypi
  • mmcv *
  • torch *
  • torchvision *
requirements/runtime.txt pypi
  • SimpleITK *
  • addict *
  • einops *
  • matplotlib *
  • numpy *
  • pycocotools *
  • six *
  • terminaltables *
  • timm *
requirements/tests.txt pypi
  • 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
requirements.txt pypi
  • SimpleITK *
  • einops *
  • pandas *
  • scipy *
  • timm *
  • xlsxwriter *
.github/workflows/build.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • codecov/codecov-action v1.0.10 composite
  • codecov/codecov-action v2 composite
.github/workflows/build_pat.yml actions
  • actions/checkout v2 composite
.github/workflows/deploy.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/lint.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
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
setup.py pypi