Evaluation of QCNN-LSTM for Disability Forecasting in Multiple Sclerosis Using Sequential Multisequence MRI

• URL: http://arxiv.org/abs/2401.12132v1
• Date: Mon, 22 Jan 2024 17:14:47 GMT
• Title: Evaluation of QCNN-LSTM for Disability Forecasting in Multiple Sclerosis Using Sequential Multisequence MRI
• Authors: John D. Mayfield and Issam El Naqa
• Abstract summary: Long ShortTerm Memory (LSTM) models were studied to provide sequential relationships for each timepoint in MRIs of patients with Multiple Sclerosis (MS) Our hypothesis is that quantum models will provide competitive performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Adaptive Self-Supervised Consistency-Guided Diffusion Model for Accelerated MRI Reconstruction [1.167578793004766]
  We propose a self-Supervised deep learning compressed Diffusion sensing MRI (DL)" method. We used 1,376 and singlecoil brain axial post T1 dataset (T1-w) 50 patients. It was compared with ReconFormer Transformer and SS-MRI, assessing performance using normalized mean error (NMSE), peak signal-to-noise ratio (PSNR), and similarity index (SSIM)
  arXiv  Detail & Related papers  (2024-06-21T21:22:17Z)
• Deep Learning Classification of Photoplethysmogram Signal for Hypertension Levels [2.289386500039994]
  The classification has been done for two categories: Prehypertension (normal levels) and Hypertension (includes Stage I and Stage II) With precision and specificity of 100% and recall of 82.1%, the LSTM model provides the best results among all combinations of Neural Networks. The maximum accuracy of 71.9% is achieved by the LSTM-CNN model.
  arXiv  Detail & Related papers  (2024-05-23T13:35:53Z)
• Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma [4.578027879885667]
  This research aims to improve glioblastoma survival prediction by integrating MR images, clinical and molecular-pathologic data in a transformer-based deep learning model. The model employs self-supervised learning techniques to effectively encode the high-dimensional MRI input for integration with non-imaging data using cross-attention.
  arXiv  Detail & Related papers  (2024-05-21T17:44:48Z)
• Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion [56.38386580040991]
  Consistency Trajectory Model (CTM) is a generalization of Consistency Models (CM) CTM enables the efficient combination of adversarial training and denoising score matching loss to enhance performance. Unlike CM, CTM's access to the score function can streamline the adoption of established controllable/conditional generation methods.
  arXiv  Detail & Related papers  (2023-10-01T05:07:17Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Bayesian Neural Network Language Modeling for Speech Recognition [59.681758762712754]
  State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. In this paper, an overarching full Bayesian learning framework is proposed to account for the underlying uncertainty in LSTM-RNN and Transformer LMs.
  arXiv  Detail & Related papers  (2022-08-28T17:50:19Z)
• Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease [3.651988141747362]
  This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to prediction of conversion to AD in individuals with mild cognitive impairment (MCI) We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal prevalidation or more extensive prevalidation.
  arXiv  Detail & Related papers  (2020-12-16T07:09:30Z)
• Intra-Domain Task-Adaptive Transfer Learning to Determine Acute Ischemic Stroke Onset Time [7.024121839235693]
  Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS) Current clinical guidelines for patients with unknown TSS recommend the use of MRI to determine eligibility for thrombolysis, but radiology assessments have high inter-reader variability. We present deep learning models that leverage MRI diffusion series to classify TSS based on clinically validated thresholds.
  arXiv  Detail & Related papers  (2020-11-05T18:28:54Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.