While deep learning excels in many areas, its application in medicine is hindered by limited data, which restricts model generalizability. Few-shot learning has emerged as a potential solution to this problem. In this work, we leverage the strengths of meta-learning, the primary framework for few-shot learning, along with diffusion-based generative models to enhance few-shot learning capabilities. We propose a novel method that jointly trains a diffusion model and a feature extractor in an episodic-based manner. The diffusion model learns conditional generation based on each episode’s support samples. After updating its parameters, it generates additional support samples for each class. The augmented support set is used to train a feature extractor within a prototypical meta-learning framework. Notably, we propose a weighted prototype computation based on the distance between each generated sample and the original class prototype, i.e., derived solely from the original support samples. Evaluations on two tumor characterization tasks (prostate cancer aggressiveness and breast cancer malignity assessment) demonstrate our approach’s effectiveness in improving prototype representation and boosting classification performance. Find our code at: https://github.com/evapachetti/meta_diffusion .