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A Deep Bayesian Convolutional Spiking Neural Network-based CAD system with Uncertainty Quantification for Medical Images Classification

arXiv 2504.17819 ↗First posted 23 Apr 2025eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

The Computer_Aided Diagnosis (CAD) systems facilitate accurate diagnosis of diseases. The development of CADs by leveraging third generation neural network, namely, Spiking Neural Network (SNN), is essential to utilize of the benefits of SNNs, such as their event_driven processing, parallelism, low power consumption, and the ability to process sparse temporal_spatial information. However, Deep SNN as a deep learning model faces challenges with unreliability. To deal with unreliability challenges due to inability to quantify the uncertainty of the predictions, we proposed a deep Bayesian Convolutional Spiking Neural Network based_CADs with uncertainty_aware module. In this study, the Monte Carlo Dropout method as Bayesian approximation is used as an uncertainty quantification method. This method was applied to several medical image classification tasks. Our experimental results demonstrate that our proposed model is accurate and reliable and will be a proper alternative to conventional deep learning for medical image classification.

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References · 12

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  3. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  4. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
  5. 2020Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Userscited by 338
  6. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 259
  7. 2021Accurate and efficient time-domain classification with adaptive spiking recurrent neural networkscited by 115
  8. 2021A Spiking Neural Network for Image Segmentationcited by 27
  9. 2021UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detectioncited by 8
  10. 2023Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropoutcited by 5
  11. 2024Reliable Breast Cancer Molecular Subtype Prediction based on uncertainty-aware Bayesian Deep Learning by Mammographycited by 2
  12. 2025BI-RADS prediction of mammographic masses using uncertainty information extracted from a Bayesian Deep Learning modelcited by 1

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