Evaluation of QCNN-LSTM for Disability Forecasting in Multiple Sclerosis
  Using Sequential Multisequence MRI
John D. Mayfield Issam El Naqa
Introduction Quantum Convolutional Neural Network (QCNN)-Long Short-Term
Memory (LSTM) models were studied to provide sequential relationships for each
timepoint in MRIs of patients with Multiple Sclerosis (MS). In this pilot
study, we compared three QCNN-LSTM models for binary classification of MS
disability benchmarked against classical neural network architectures. Our
hypothesis is that quantum models will provide competitive performance. Methods
Matrix Product State (MPS), reverse Multistate Entanglement Renormalization
Ansatz (MERA), and Tree-Tensor Network (TTN) circuits were paired with LSTM
layer to process near-annual MRI data of patients diagnosed with MS. These were
benchmarked against a Visual Geometry Group (VGG)-LSTM and a Video Vision
Transformer (ViViT). Predicted logits were measured against ground truth labels
of each patient's Extended Disability Severity Score (EDSS) using binary
cross-entropy loss. Training/validation/holdout testing was partitioned using
5-fold cross validation with a total split of 60:20:20. Levene's test of
variance was used to measure statistical difference and Student's t-test for
paired model differences in mean. Results The MPS-LSTM, reverse MERA-LSTM, and
TTN-LSTM had holdout testing ROC-AUC of 0.70, 0.77, and 0.81, respectively
(p-value 0.915). VGG16-LSTM and ViViT performed similarly with ROC-AUC of 0.73
and 0.77, respectively (p-value 0.631). Overall variance and mean were not
statistically significant (p-value 0.713), however, time to train was
significantly faster for the QCNN-LSTMs (39.4 sec per fold vs. 224 and 218,
respectively, p-value <0.001). Conclusion QCNN-LSTM models perform
competitively to their classical counterparts with greater efficiency in train
time. Clinically, these can add value in terms of efficiency to time-dependent
deep learning prediction of disease progression based upon medical imaging.