J. Imaging 2023, 9(7), 128; https://doi.org/10.3390/jimaging9070128
CSV files are available in the subfolders of the chexpert-corrected folder, organized to comply with the codes.
These subfolders must be populated by the corresponding images to download from: https://stanfordaimi.azurewebsites.net/datasets/8cbd9ed4-2eb9-4565-affc-111cf4f7ebe2 after login and agreeing to the Stanford University Dataset Research Use Agreement.
Author: @poig
Notebook Version:
ten-fold-cross-validation-cc-densenet-121.ipynb
Command Prompt Version:
command prompt/trainer.py : the main program here you will execute in command prompt
command prompt/requirements.txt : requirement package install with conda create --name <env> --file <this file>
command prompt/command : example how to start training, require modify address before execute
Authors: @poig & @pdc-quantum
Saliency maps obtained using GradCAM++ for the test dataset are collected in folder gradcam_final.
- Subfolder gcnn is for the classical-classical model.
- Subfolder gpnn is for the PennyLane-based classical-quantum model.
- Subfolder gqnn is for the Qiskit-based classical-quantum model.
The notebooks that generated these collections are:
saliency-maps-test-set-classical-model.ipynb
saliency-maps-test-set-6-qubit-qiskit-model.ipynb
saliency-maps-test-set-6-qubit-pennylane-model.ipynb
After cloning the repository to your local system, create a virtual environment, and activate it.
conda create --name <env_name> python=3.8
On Windows:
.\<env_name>\Scripts\activate
On Mac/Linux:
source ./<env_name>/bin/activate
Then install the required packages using the specified requirements.txt file
conda install -n <env_name> requirements.txt
Run trainer.py file
command prompt/trainer.py
An example on how to start training, the address must be modified before execution
command prompt/command