quantum-transfer-learning-metastases
Quantum Transfer Learning for Lymph Node Metastases Detection
https://github.com/reshalfahsi/quantum-transfer-learning-metastases
Science Score: 54.0%
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Repository
Quantum Transfer Learning for Lymph Node Metastases Detection
Basic Info
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Metadata Files
README.md
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 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.
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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
- PatchCamelyon (PCam)
- Rotation Equivariant CNNs for Digital Pathology
- Transfer learning in hybrid classical-quantum neural networks
- Quantum embeddings for machine learning
- Quantum algorithms for electronic structure calculations: particle/hole Hamiltonian and optimized wavefunction expansions
- PennyLane: Automatic differentiation of hybrid quantum-classical computations
- Turning quantum nodes into Torch Layers
- PyTorch Lightning
Owner
- Name: Resha Dwika Hefni Al-Fahsi
- Login: reshalfahsi
- Kind: user
- Location: Yogyakarta, Indonesia
- Website: reshalfahsi.github.io
- Twitter: reshalfahsi
- Repositories: 80
- Profile: https://github.com/reshalfahsi
Experienced Tensorbender Strolling in the Latent Space
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you find this repository useful for your research, please cite it:"
title: "Quantum Transfer Learning for Lymph Node Metastases Detection"
authors:
- family-names: Al-Fahsi
given-names: Resha Dwika Hefni
url: https://github.com/reshalfahsi/quantum-transfer-learning-metastases
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