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Related Experiment Videos

An uncertainty-aware vision transformer-BiLSTM Bayesian framework for reliable clinical decision support using chest

Fuad S Al-Duais1, Sahar Almenwer2, Afrah Alanazi3

  • 1Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia. F.alduais@psau.edu.sa.

Scientific Reports
|June 16, 2026
PubMed
Summary

Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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This summary is machine-generated.

This study introduces a Vision Transformer-BiLSTM Bayesian Fusion (ViT-BiLSTM-BF) model for reliable chest X-ray analysis, significantly improving disease detection accuracy and uncertainty estimation in medical imaging.

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Deep Learning for Diagnostics

Background:

  • Deep learning models in medical imaging face challenges in reliability and uncertainty estimation.
  • Conventional Convolutional Neural Network (CNN)-based models often exhibit extreme prediction confidence, limiting clinical trust.
  • Accurate uncertainty quantification is crucial for high-stakes diagnostic decisions in clinical practice.

Purpose of the Study:

  • To develop an uncertainty-aware deep learning framework for reliable disease detection using chest X-rays.
  • To enhance the interpretability and trustworthiness of AI models in clinical decision support.
  • To address limitations in current deep learning models regarding reliability and confidence estimation.

Main Methods:

Keywords:
Bayesian fusionChest X-raysClinical decision supportMedical imagingUncertainty estimationVision transformer

Related Experiment Videos

  • Proposed a Vision Transformer-BiLSTM Bayesian Fusion (ViT-BiLSTM-BF) architecture.
  • Utilized chest X-rays from MIMIC-CXR-JPG and PadChest-GR datasets for training and evaluation.
  • Incorporated U-Net based lung segmentation, data augmentation, and a Bayesian fusion layer for uncertainty quantification.

Main Results:

  • Achieved superior performance with 95.5% accuracy and 0.986 AUC-ROC.
  • Demonstrated extremely small calibration error (ECE = 0.028), indicating well-calibrated confidence measures.
  • Outperformed existing models including CNN, DenseNet, Bayesian CNN, and individual ViT models.

Conclusions:

  • The ViT-BiLSTM-BF model offers a highly reliable and clinically meaningful diagnostic system for chest X-ray analysis.
  • The framework effectively addresses critical issues of uncertainty estimation and model reliability in medical AI.
  • The proposed approach shows potential for feasible implementation in clinical decision support systems.