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https://github.com/google-deepmind/codoc
https://github.com/google-deepmind/codoc
Last synced: 7 days ago
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- Host: GitHub
- URL: https://github.com/google-deepmind/codoc
- Owner: google-deepmind
- License: apache-2.0
- Created: 2023-07-06T13:17:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-07-14T11:36:55.000Z (over 1 year ago)
- Last Synced: 2024-08-02T15:32:56.602Z (3 months ago)
- Language: Python
- Size: 337 KB
- Stars: 113
- Watchers: 11
- Forks: 21
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# Repository for Complementarity-Driven Deferral to Clinicians (CoDoC)
This repository includes the source code for the paper "Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians (CoDoC)" by Dvijotham et al. (2023), published in the journal _Nature Medicine_. The contents of the repository can be used to replicate the experiments provided in the paper, as well as to utilize the CoDoC framework in independent human-AI complementarity research.
## Installation
The following command sets up python virtual environment,
```bash
python3 -m venv codoc_env
source codoc_env/bin/activate
```
This uses `virtualenv` python module
to create virtual environment. If it doesn not exist, please install it with
`pip`. Once the environment is set up we can proceed to install all the required
dependencies.
```bash
pip install -r requirements.txt
```
## Running
```bash
bash run.sh
```
The above script should open a notebook server from which `codoc_experiments.ipynb`
can be run. The notebook has further instructions and documentation to guide
through running the experimentation pipeline.
## Quickstart
For both purposes mentioned above, we recommend starting from the Jupyter notebook file `Replicating_CoDoC_Experiment_Results.ipynb`. This file walks the user through various functionalities of the implementation provided, familiarizes them with the data format adopted, and if desired provides more specific instructions for the exact replication of existing results.
Please refer to the original paper for a detailed introduction to the CoDoC framework, its clinical and statistical properties, and experimental results on a variety of datasets.
## Datasets
The UK Mammography Dataset (AI scores, clinical predictions, ground truth) is used for generating the results in the paper. If you're interested in the data, please email [email protected] and you will be contacted once it is available.
The US Mammography Dataset 2 can be obtained for research purposes by contacting Prof. Krzysztof J Geras ([email protected]).
## Contact
For any questions regarding this repository or the paper, please contact Krishnamurthy (Dj) Dvijotham ([email protected]) and Jim Winkens ([email protected]).
## License
Copyright 2023 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
## Disclaimer
The content of this research code repository (i) may not be used as a medical device; (ii) may be not used for clinical use of any kind, including but not limited to diagnosis or prognosis; and (iii) may not be used to generate any identifying information about an individual.