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A reinforcement learning model for AI-based decision support in skin cancer

Overview

This repository contains the source code to implement a deep-Q learning model that incorporates human-values in the diagnostic/management process of melanoma patients. The model receives features from a standard neural network (e.g. CNN) as well as the softmax probabilities for the different classes. It then makes a recommendation, based on the policy learned from a reward table defined by medical experts. The model may keep or change the diagnosis when compared witht the CNN. Additionally, we consider the possibility for the model to recommend management actions.

System Requirements

Most experiments were run using a desktop with 16Gb of RAM, an Intel i5-7600 CPU @350HZ, and an NIVIDA Titan Xp. However, given the type of data and the characterisitcs of the deep Q-learning model, our demo can be easily run in a regular desktop computer without GPU. The user must only guarantee a minimum of 16Gb of RAM to accomodate both model and data.

The models were trained on Microsoft Windows 10 Pro 64bit and Ubunto 18.04

Installation

Data preparation

  • Download the datasets to the data folder, as described in the data/Data_Download.txt file

Run Demo - RL for Diagnosis

To run the demo (train an RL agent and make predictions on a validation set):

  1. Go to the corresponding directory

  2. Run the python RL_Skin_Cancer_Demo_Diagnosis.py

  3. You can manipulate the number of patients per episode (episode length/number of iterations), the number of episodes, and whether to use or not the unknown action. An example: python RL_Skin_Cancer_Demo_Diagnosis.py --n_patients 100 --n_episodes 150 --use_unknown False

  4. We give the possibility to try with different reward tables, where the penalty for the use of the Unknown class is changed - simply (un)comment the code of the desired reward in funtion step (class Dermatologist). You can also use this function to define new reward tables

Run Demo - RL for lesion/image-level management

To run the demo (train an RL agent and make predictions on a validation set):

  1. Go to the corresponding directory

  2. Run the python RL_Skin_Cancer_Demo_Management.py

  3. You can manipulate the number of patients per episode (episode length/number of iterations), the number of episodes, and the number of actions (2 - dismiss/excise or 3 dismiss/treat locally/excise). An example: python RL_Skin_Cancer_Demo_Management.py --n_patients 100 --n_episodes 150 --n_actions 2

  4. We give the possibility to try with different expert reward tables for the 2 actions problem - simply (un)comment the code of the desired reward in funtion step (class Dermatologist). You can also use this function to define new reward tables

Run Demo - RL for patient-level management

To run the demo (train an RL agent and make predictions on a validation set):

  1. Go to the corresponding directory

  2. Run the python Skin_Cancer_RL_Demo_Patient_Management.py

  3. You can manipulate the number of patients seen before updating Q-network, the number of macro episodes - i.e. the number of times the full dataset is run and the number of actions (2 - dismiss/excise or 3 dismiss/monitor/excise). An example: python Skin_Cancer_RL_Demo_Patient_Management.py --n_patients 1 --n_episodes 130 --n_actions 3

Expected Outcome - You should be able to train a RL models that are able to predict the Q-values of the different actions (diagnostic/management decisions) given the features and softmax probabilities of a standard supervised model. The model then chooses the action with the highest Q-value. A numerical evaluation is carried out using the confusion matrix to show the performance of the RL model on a validation set.

Try your data

To try new data, some modifications must be done:

  1. If using the same 7 classes of skin lesions, but different images and/or different CNN - you just need to save the features into a numpy array and the probabilities, image id, and real diagnosis into a CSV. Please check the formats used in the demo examples (data folder). If working with the patient-level setting, you will need to creat a numpy matrix with the following structure (n_patients x n_images x features) and the corresponding ground truth diagnosis - see the provided examples patient_embeddings.npy and gt_patients_embeddings.

  2. For new (medical) problems, you will also need to adjust the initialize_clinical_practice to your dataset, as well as the reward tables. You may also need to adjust the Q-network with additional layers (function create_q_model).

Reference

@article{barata2023reinforcement,
  title={A reinforcement learning model for AI-based decision support in skin cancer},
  author={Barata, Catarina and Rotemberg, Veronica and Codella, Noel CF and Tschandl, Philipp and Rinner, Christoph and Akay, Bengu Nisa and Apalla, Zoe and Argenziano, Giuseppe and Halpern, Allan and Lallas, Aimilios and others},
  journal={Nature Medicine},
  pages={1--6},
  year={2023},
  publisher={Nature Publishing Group US New York}
}