ZERO-SHOT AND FEW-SHOT LEARNING IN MEDICAL IMAGING USING DIFFUSION MODELS

Abstract : Zero-shot and few-shot learning in medical imaging represent transformative approaches that address the critical challenge of limited annotated data, which is often expensive, time-consuming, and requires expert knowledge to obtain. Leveraging the capabilities of generative diffusion models offers a promising solution by synthesizing high-quality, diverse, and realistic medical images that can be used to augment scarce datasets or directly support model training. Diffusion models, which iteratively refine random noise into coherent images, have demonstrated exceptional performance in capturing complex data distributions, making them particularly suitable for the nuanced patterns found in medical imaging modalities such as MRI, CT, and X-ray. In zero-shot learning, diffusion models can be conditioned on textual or semantic prompts to generate medical images for unseen classes, effectively enabling model training or evaluation without any direct exposure to labeled examples. For few-shot learning, the generative capabilities of diffusion models can be harnessed to amplify small annotated datasets by producing variations that preserve diagnostic features while introducing controlled diversity. These synthetic samples can then be used to fine-tune classification, segmentation, or detection models, leading to improved generalization and robustness. Furthermore, diffusion-based frameworks can be integrated with contrastive learning or self-supervised pretraining to enhance feature extraction from limited data, further bridging the gap between model performance and data availability. Recent advancements, such as conditioning diffusion models on clinical metadata or anatomical priors, allow for more targeted and clinically valid sample generation, reducing the risk of generating implausible or biased outputs. Evaluation of such systems typically involves comparing performance against baseline models trained on the same small datasets without augmentation, with results showing significant gains in accuracy, sensitivity, and robustness to data distribution shifts. Despite their promise, the deployment of diffusion models in clinical practice requires careful validation, particularly in ensuring that synthetic images do not inadvertently introduce diagnostic errors or mislead downstream models. Ethical considerations, such as transparency in synthetic data usage and mitigation of bias, are also crucial. Nonetheless, the synergy between zero-/few-shot learning paradigms and diffusion models represents a major leap forward in democratizing access to high-performing medical AI, especially in under-resourced settings where annotated data is scarce. Continued research in this domain holds the potential to reshape medical imaging workflows, enabling faster, more accurate, and more scalable diagnostic tools through the intelligent use of generative modelling.

Keyword Zero-shot learning, Few-shot learning, Medical imaging, Diffusion models, Generative modeling, Data augmentation, Self-supervised learning, Synthetic data