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<p>Learning-based image classification has become central to modern medical imaging, but the field is changing rapidly: foundation models, vision–language models (VLMs), and label-efficient pretraining are reshaping which methods are clinically useful. This review focuses on the state of the art rather than re-explaining well-established models. We summarize learning paradigms, contrast classical machine learning (ML) and deep learning (DL) families, and emphasize advances most relevant to clinical translation: medical foundation models, multimodal VLMs, hybrid CNN–transformer architectures, diffusion-based augmentation, self-supervised pretraining, federated learning, and efficient deployment. We also discuss modality-specific issues across X-ray, CT, MRI, PET/SPECT, ultrasound, OCT, endoscopy, microscopy, and optical/molecular/infrared imaging because model choice depends strongly on image structure, annotation cost, and workflow. Finally, we outline persistent clinical challenges, data diversity and bias, rare-condition detection, annotation noise, explainability, calibration, and equitable performance, and the methods that mitigate them. The aim is to provide biomedical engineers and clinicians with a compact, clinically grounded reference for selecting and validating AI-based classifiers for real medical workflows.</p>

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Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月16日

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  • Nafiseh Ghaffar Nia,
  • Rayyan Manwar,
  • Kamran Avanaki

摘要

Learning-based image classification has become central to modern medical imaging, but the field is changing rapidly: foundation models, vision–language models (VLMs), and label-efficient pretraining are reshaping which methods are clinically useful. This review focuses on the state of the art rather than re-explaining well-established models. We summarize learning paradigms, contrast classical machine learning (ML) and deep learning (DL) families, and emphasize advances most relevant to clinical translation: medical foundation models, multimodal VLMs, hybrid CNN–transformer architectures, diffusion-based augmentation, self-supervised pretraining, federated learning, and efficient deployment. We also discuss modality-specific issues across X-ray, CT, MRI, PET/SPECT, ultrasound, OCT, endoscopy, microscopy, and optical/molecular/infrared imaging because model choice depends strongly on image structure, annotation cost, and workflow. Finally, we outline persistent clinical challenges, data diversity and bias, rare-condition detection, annotation noise, explainability, calibration, and equitable performance, and the methods that mitigate them. The aim is to provide biomedical engineers and clinicians with a compact, clinically grounded reference for selecting and validating AI-based classifiers for real medical workflows.