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We introduce LaDiNE, a novel ensemble learning method for medical image analysis. This approach combines Vision Transformers and diffusion models to enhance prediction accuracy and confidence calibration, improving reliability in clinical applications.

Area of Science:

Medical Image Analysis
Deep Learning
Computer Vision

Background:

Deep learning models for medical imaging struggle with real-world data corruptions and noise.
Unreliable predictions and poor confidence calibration hinder clinical adoption of AI in medicine.
Existing methods address specific issues like robustness or calibration but lack a unified framework.

Purpose of the Study:

To develop a novel ensemble learning framework, LaDiNE, for robust and reliable medical image classification.
To improve prediction accuracy and confidence calibration under various image perturbations.
To bridge the gap between deep learning performance on clean data and real-world clinical applicability.

Main Methods:

LaDiNE employs an ensemble learning approach combining Vision Transformers and diffusion-based generative models.

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Vision Transformer encoder blocks extract hierarchical, invariant features robust to perturbations.

Diffusion models act as density estimators for improved data distribution modeling and confidence calibration.

Main Results:

LaDiNE demonstrated superior performance over state-of-the-art methods on tuberculosis and melanoma datasets.
The method simultaneously improved prediction accuracy and confidence calibration.
Effectiveness was shown under unseen noise, adversarial perturbations, and resolution degradation.

Conclusions:

LaDiNE offers a robust and reliable solution for medical image classification in the presence of data corruptions.
The combined approach of Vision Transformers and diffusion models enhances model generalizability and trustworthiness.
This framework has the potential to significantly advance the clinical applicability of AI in medical diagnostics.