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Confidence-Guided Adaptive Diffusion Network for Medical Image Classification.

Yang Yan¹, Zhuo Xie¹, Wenbo Huang¹

¹School of Computer Science and Technology, Changchun Normal University, Changchun 130032, China.

Journal of Imaging

|February 26, 2026

Summary

This summary is machine-generated.

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This study introduces a Confidence-Guided Adaptive Diffusion Network (CGAD-Net) for improved medical image classification. The novel approach enhances feature representation and model stability by adaptively adjusting noise during the diffusion process.

Area of Science:

Medical Image Analysis
Artificial Intelligence in Healthcare
Computer Vision

Background:

Medical image classification is crucial for clinical applications like disease screening and detection.
Diffusion models show promise for medical image classification due to strong representation learning.
Existing methods struggle with multi-scale information, contextual dependencies, and uniform noise injection, limiting performance.

Purpose of the Study:

To address limitations in current diffusion-based medical image classification.
To propose a novel network, CGAD-Net, for enhanced classification accuracy and robustness.
To improve feature representation by capturing multi-scale semantics and contextual information.

Main Methods:

Introduced a hybrid prior modeling framework with Hierarchical Pyramid Context Modeling (HPCM) and Intra-Scale Dilated Convolution Refinement (IDCR) modules.

Keywords:

confidence-guided noise injection diffusion models medical image classification multi-scale semantic modeling prior-guided diffusion

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Developed a Confidence-Guided Adaptive Noise Injection (CG-ANI) strategy to dynamically adjust noise based on sample confidence.

Implemented CGAD-Net for medical image classification, focusing on stabilizing training and robust representation learning.

Main Results:

CGAD-Net achieved competitive classification accuracy, robustness, and training stability on benchmarks like HAM10000, APTOS2019, and Chaoyang.
The HPCM and IDCR modules effectively captured fine-grained structural details and global semantic information.
CG-ANI successfully stabilized training and enhanced representation learning for ambiguous samples.

Conclusions:

CGAD-Net demonstrates the effectiveness of confidence-guided diffusion modeling for 2D medical image classification.
The proposed methods offer significant improvements in discriminative power and generalization ability.
This work provides valuable insights for advancing diffusion models in medical image analysis.