https://github.com/adgaudio/o-medal
O-MedAL: Online Active Deep Learning for Medical Image Analysis. This repo contains code for the paper.
https://github.com/adgaudio/o-medal
active-learning deep-learning diabetic-retinopathy-detection machine-learning medical-image-analysis online-learning
Last synced: 29 days ago
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O-MedAL: Online Active Deep Learning for Medical Image Analysis. This repo contains code for the paper.
- Host: GitHub
- URL: https://github.com/adgaudio/o-medal
- Owner: adgaudio
- License: mit
- Created: 2019-02-01T18:17:19.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-03-20T23:06:46.000Z (about 3 years ago)
- Last Synced: 2025-04-30T06:08:02.337Z (29 days ago)
- Topics: active-learning, deep-learning, diabetic-retinopathy-detection, machine-learning, medical-image-analysis, online-learning
- Language: Python
- Homepage:
- Size: 2.64 MB
- Stars: 18
- Watchers: 3
- Forks: 12
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# O-MedAL: Online Active Deep Learning for Medical Image Analysis
Code for the paper, written with pytorch.
[on arXiv](https://arxiv.org/abs/1908.10508)
[on Wiley Journal of Data Mining and Knowledge Discovery](https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1353)
## Setup
Git clone the repo, and navigate your shell to the root of the repo.
Download the Messidor dataset to ./data/messidor/
$ ls data/messidor
Annotation_Base11.csv Annotation_Base14.csv Annotation_Base23.csv Annotation_Base32.csv Base11 Base14 Base23 Base32
Annotation_Base12.csv Annotation_Base21.csv Annotation_Base24.csv Annotation_Base33.csv Base12 Base21 Base24 Base33
Annotation_Base13.csv Annotation_Base22.csv Annotation_Base31.csv Annotation_Base34.csv Base13 Base22 Base31 Base34
Link your ~/.torch to ./data/torch (avoid downloading pre-trained if you don't need to)
$ ln -sr ~/.torch ./data/torch
Install missing python requirements (if necessary)
$ cat ./requirements.txt
You should have a gpu on the machine too! Check with:
$ nvidia-smi
## Usage
From the root of this repo, type:
python -m medal OnlineMedalResnet18BinaryClassifier --run-id test -h
python -m medal -h
## The code structure:
- `medal/model_configs/medal.py` - **the primary source code of
interest for this paper.**
- `medal/model_configs` - contains the collection of model, loss
function, default hyperparameters, data loader, etc. Note that any
class variables defined here are magically exposed to the
commandline, which is very handy for quick experimentation :)
- `medal/datasets.py` - the pytorch DataSet for Messidor
- `medal/models` - contains some simple pytorch model files
## Reproducibility
The experiments over values of p used to produce the final online
medal results is `./bin/reproduce_paper_results.sh`. You may need to
just run the "python -m ..." bit. Sorry if this is confusing.
Also keep in mind if reproducing that we fixed Messidor errata for the
online portion of these results, as mentioned in the paper.
I can create a separate GitHub repo to share about 100mb of precisely
detailed log files and post-analysis data. If there is interest for
this, please open an issue.
I created a git tag to synchronize the code with published version on
arXiv.
## Questions?
Please feel free to open an issue or send me an email.