Summary of Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning For Long-tailed Chest X-ray Classification, by Skylar Chan et al.
Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification
by Skylar Chan, Pranav Kulkarni, Paul H. Yi, Vishwa S. Parekh
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Quantum Physics (quant-ph)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the potential benefits of quantum machine learning (QML) in improving the multi-label classification of rare diseases using large-scale chest x-ray (CXR) datasets. QML has theoretical advantages over classical machine learning (CML) in terms of sample efficiency and generalizability, making it a promising approach for complex tasks like long-tailed classification. The authors implemented a Jax-based framework that simulates medium-sized qubit architectures, achieving significant speed-ups compared to PyTorch and TensorFlow implementations. They evaluated the performance of their framework using hybrid quantum transfer learning on 8, 14, and 19 disease labels across large-scale CXR datasets, observing up to a 95% speed-up over CML. While QML demonstrated slower convergence and slightly lower AUROC scores compared to CML, this work presents an accessible implementation of hybrid quantum transfer learning for long-tailed CXR classification with a computationally efficient Jax-based framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using powerful computers that can help doctors diagnose diseases better. This paper looks at how these computers, called quantum machines, can be used to improve the diagnosis of rare diseases from chest x-ray images. The authors created a special program that makes it easier and faster to use these quantum machines for this task. They tested their program on lots of different diseases and found that it was much faster than other programs, but not always as good at making accurate diagnoses. Overall, this work shows how quantum computers can be used to help doctors diagnose diseases more effectively. |
Keywords
» Artificial intelligence » Classification » Machine learning » Transfer learning
