https://github.com/reshalfahsi/quantum-transfer-learning-metastases

Quantum Transfer Learning for Lymph Node Metastases Detection
https://github.com/reshalfahsi/quantum-transfer-learning-metastases

googlenet inception inceptionv1 lymph-node-metastasis medical-image-classification pennylane pytorch pytorch-lightning quantum-computing quantum-transfer-learning

Last synced: 21 days ago
JSON representation

Quantum Transfer Learning for Lymph Node Metastases Detection

• Host: GitHub
• URL: https://github.com/reshalfahsi/quantum-transfer-learning-metastases
• Owner: reshalfahsi
• Created: 2024-11-25T23:53:51.000Z (29 days ago)
• Default Branch: master
• Last Pushed: 2024-11-25T23:59:06.000Z (29 days ago)
• Last Synced: 2024-11-26T00:30:00.098Z (29 days ago)
• Topics: googlenet, inception, inceptionv1, lymph-node-metastasis, medical-image-classification, pennylane, pytorch, pytorch-lightning, quantum-computing, quantum-transfer-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Citation: CITATION.cff

Awesome Lists containing this project

README

        
# Quantum Transfer Learning for Lymph Node Metastases Detection



    

    




  
 The Quantum GoogLeNet model. The quantum layer: the QAOA-inspired ansatz embedding, the particle-conserving entangler, and the expectation value of the Pauli Z operator. 


Transfer learning may make training on a particularly distinguishable dataset easier. It enables several elements of a pre-trained model to be used as the foundation of a new model's architecture. More importantly, we can adopt this approach in quantum machine learning as well. In this project, we seek to implement quantum transfer learning using an ImageNet-pre-trained model, which will be used on the PCam dataset to tackle the lymph node metastases detection problem. The pre-trained model is GoogLeNet (i.e., Inception V1), and the classifier uses hybrid classical-quantum fully connected layers. Typically, quantum layers are made up of embedding, quantum circuits, and measurement. The embedding and quantum circuits are built upon the QAOA-inspired ansatz and particle-conserving entangler, respectively.

## Experiment

Consider exploring this [notebook](https://github.com/reshalfahsi/quantum-transfer-learning-metastases/blob/master/Quantum_Transfer_Learning_for_Lymph_Node_Metastases_Detection.ipynb) to conduct the experiment by yourself.

## Result

## Quantitative Result

The quantitative results are outlined in the following table.

Test Metric | Score |

----------- | ----- |

Accuracy | 80.29%

Loss | 0.464

## Accuracy and Loss Curves

  
 The model's loss curve on the train and validation sets. 


  
 The model's accuracy curve on the train and validation sets. 


## Qualitative Result

This 3×3 image grid presents the qualitative result.

  
 . 


## Citation

If you find this repository useful for your research, please cite it:

```

@misc{quantum-transfer-learning-metastases,

   title = {Quantum Transfer Learning for Lymph Node Metastases Detection},

   url = {https://github.com/reshalfahsi/quantum-transfer-learning-metastases},

   author = {Resha Dwika Hefni Al-Fahsi},

}

```

## Credit

- [Going deeper with convolutions](https://arxiv.org/pdf/1409.4842)

- [PatchCamelyon (PCam)](https://github.com/basveeling/pcam)

- [Rotation Equivariant CNNs for Digital Pathology](https://arxiv.org/pdf/1806.03962)

- [Transfer learning in hybrid classical-quantum neural networks](https://arxiv.org/pdf/1912.08278)

- [Quantum embeddings for machine learning](https://arxiv.org/pdf/2001.03622)

- [Quantum algorithms for electronic structure calculations: particle/hole Hamiltonian and optimized wavefunction expansions](https://arxiv.org/pdf/1805.04340)

- [PennyLane: Automatic differentiation of hybrid quantum-classical computations](https://arxiv.org/pdf/1811.04968)

- [Turning quantum nodes into Torch Layers](https://pennylane.ai/qml/demos/tutorial_qnn_module_torch)

- [PyTorch Lightning](https://lightning.ai/docs/pytorch/latest/)