/cardiomegaly-chest-x-ray

hybrid classical-quantum transfer learning for cardiomegaly detection on chest x-rays

Primary LanguageJupyter NotebookApache License 2.0Apache-2.0

Hybrid Classical-Quantum Transfer Learning for Cardiomegaly Detection on Chest X-Rays

Open Access Article

J. Imaging 2023, 9(7), 128; https://doi.org/10.3390/jimaging9070128

Dataset

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.

Code

Author: @poig

Notebook Version:

init_freezer.ipynb

no freezer.ipynb

init_freezer - P6qubit.ipynb

init_freezer - P8qubit.ipynb

init_freezer - P10qubit.ipynb

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

Saliency maps

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