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.
The following command sets up python virtual environment,
python3 -m venv codoc_env
source codoc_env/bin/activateThis 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.
pip install -r requirements.txtbash run.shThe 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.
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.
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]).
For any questions regarding this repository or the paper, please contact Krishnamurthy (Dj) Dvijotham ([email protected]) and Jim Winkens ([email protected]).
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.
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.