Aligning Synthetic Medical Images with Clinical Knowledge using Human
  Feedback
Shenghuan Sun Gregory M. Goldgof Atul Butte Ahmed M. Alaa
Generative models capable of capturing nuanced clinical features in medical
images hold great promise for facilitating clinical data sharing, enhancing
rare disease datasets, and efficiently synthesizing annotated medical images at
scale. Despite their potential, assessing the quality of synthetic medical
images remains a challenge. While modern generative models can synthesize
visually-realistic medical images, the clinical validity of these images may be
called into question. Domain-agnostic scores, such as FID score, precision, and
recall, cannot incorporate clinical knowledge and are, therefore, not suitable
for assessing clinical sensibility. Additionally, there are numerous
unpredictable ways in which generative models may fail to synthesize clinically
plausible images, making it challenging to anticipate potential failures and
manually design scores for their detection. To address these challenges, this
paper introduces a pathologist-in-the-loop framework for generating
clinically-plausible synthetic medical images. Starting with a diffusion model
pretrained using real images, our framework comprises three steps: (1)
evaluating the generated images by expert pathologists to assess whether they
satisfy clinical desiderata, (2) training a reward model that predicts the
pathologist feedback on new samples, and (3) incorporating expert knowledge
into the diffusion model by using the reward model to inform a finetuning
objective. We show that human feedback significantly improves the quality of
synthetic images in terms of fidelity, diversity, utility in downstream
applications, and plausibility as evaluated by experts.