Summary of Evaluation Of Qcnn-lstm For Disability Forecasting in Multiple Sclerosis Using Sequential Multisequence Mri, by John D. Mayfield and Issam El Naqa
Evaluation of QCNN-LSTM for Disability Forecasting in Multiple Sclerosis Using Sequential Multisequence MRI
by John D. Mayfield, Issam El Naqa
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Image and Video Processing (eess.IV)
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| 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 study explores the application of Quantum Convolutional Neural Network (QCNN)-Long Short-Term Memory (LSTM) models for analyzing Magnetic Resonance Imaging (MRI) data of patients with Multiple Sclerosis (MS). The authors compare three QCNN-LSTM models to classical neural network architectures, such as Visual Geometry Group (VGG)-LSTM and Video Vision Transformer (ViViT), for binary classification of MS disability. The study uses Matrix Product State (MPS), reverse Multistate Entanglement Renormalization Ansatz (MERA), and Tree-Tensor Network (TTN) circuits paired with an LSTM layer to process near-annual MRI data. The authors find that the QCNN-LSTM models perform competitively with their classical counterparts, achieving holdout testing ROC-AUC scores of 0.70 to 0.81, while also exhibiting faster training times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at a new way to analyze pictures of people’s brains taken by MRI machines. The goal is to see if this new way can help doctors predict how well someone with Multiple Sclerosis will be able to move and walk in the future. The researchers compared their new method, called Quantum Convolutional Neural Network (QCNN), to other ways that are already used. They found that the QCNN worked just as well as the other methods, but it was faster and might be more helpful for doctors. |
Keywords
* Artificial intelligence * Auc * Classification * Lstm * Neural network * Vision transformer
