Generative models have proven to be very effective in generating synthetic medical images and find applications in downstream tasks such as enhancing rare disease datasets, long-tailed dataset augmentation, and scaling machine learning algorithms. For medical applications, the synthetically generated medical images by such models are still reasonable in quality when evaluated based on traditional metrics such as FID score, precision, and recall. However, these metrics fail to capture the medical/biological plausibility of the generated images. Human expert feedback has been used to get biological plausibility which demonstrates that these generated images have very low plausibility. Recently, the research community has further integrated this human feedback through Reinforcement Learning from Human Feedback(RLHF), which generates more medically plausible images. However, incorporating human feedback is a costly and slow process. In this work, we propose a novel approach to improve the medical plausibility of generated images without the need for human feedback. We introduce IMPROVE:Improving Medical Plausibility without Reliance on Human Validation - An Enhanced Prototype-Guided Diffusion Framework, a prototype-guided diffusion process for medical image generation and show that it substantially enhances the biological plausibility of the generated medical images without the need for any human feedback. We perform experiments on Bone Marrow and HAM10000 datasets and show that medical accuracy can be substantially increased without human feedback.

翻译：生成模型在合成医学图像方面已被证明非常有效，并广泛应用于下游任务，如增强罕见疾病数据集、长尾数据集扩充以及扩展机器学习算法。在医学应用中，此类模型生成的合成医学图像在基于传统指标（如FID分数、精确率和召回率）评估时，其质量仍属合理。然而，这些指标无法捕捉生成图像的医学/生物学合理性。已有研究利用人类专家反馈来评估生物学合理性，结果表明这些生成图像的合理性非常低。近期，研究社区进一步通过人类反馈强化学习（RLHF）整合了此类反馈，从而生成更具医学合理性的图像。然而，融入人类反馈是一个成本高昂且缓慢的过程。在本研究中，我们提出了一种无需人类反馈即可提升生成图像医学合理性的新方法。我们引入了IMPROVE：无需依赖人工验证的医学合理性提升——一种增强的原型引导扩散框架，这是一种用于医学图像生成的原型引导扩散过程，并证明其能在无需任何人类反馈的情况下，显著提升生成医学图像的生物学合理性。我们在骨髓和HAM10000数据集上进行了实验，结果表明无需人类反馈即可大幅提高医学准确性。