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Updated: Jun 24, 2026

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Adaptive distribution-aware transformer for multi-scale visual representation learning on imbalanced and

Sakib Ahammed¹, Xia Cui¹, Wenqi Lu¹

¹Department of Computing and Mathematics, Manchester Metropolitan University, The Dalton Building, Chester Street, Manchester, M1 5GD, UK.

Medical Image Analysis

|June 22, 2026

Summary

This summary is machine-generated.

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AdaptiveViT, a new deep learning model, effectively handles imbalanced medical datasets and low-resolution images. This hybrid CNN-Transformer architecture improves classification accuracy for rare conditions like melanoma.

Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Deep learning models face challenges with class imbalance and low-resolution medical images.
Minority-class features and critical spatial details are often underrepresented, impacting diagnostic accuracy.

Purpose of the Study:

To introduce Adaptive Distribution-aware Vision Transformer (AdaptiveViT), a novel hybrid CNN-Transformer architecture.
To address class imbalance and image resolution variability in medical image classification.

Main Methods:

AdaptiveViT unifies fine-grained local feature extraction (CNN) with global contextual modeling (Transformer).
It incorporates a distribution-aware modulation mechanism and a Distribution-aware Adaptive (DA) Loss to enhance minority-class sensitivity.

Keywords:

Adaptive modulation Class imbalance Hybrid CNN-Transformer Low-resolution image Medical image classification Representation learning

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Last Updated: Jun 24, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Experiments were conducted on skin lesion and gastrointestinal endoscopy datasets with varying resolutions and imbalance ratios.

Main Results:

AdaptiveViT outperformed state-of-the-art baselines in F1 and AUC scores across multiple datasets.
The model demonstrated stable convergence across different levels of class imbalance.
Validation on endoscopy data confirmed AdaptiveViT's domain-agnostic generalization capabilities.

Conclusions:

AdaptiveViT establishes a robust hybrid framework for medical image classification, particularly under class imbalance and resolution variability.
The approach offers improved diagnostic reliability for underrepresented conditions in medical imaging.