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.

该研究提出一种 “病理学家在环路中” 的框架，通过人工专家的反馈，将临床知识注入扩散模型，从而生成具有临床可信度的合成医学影像。

利用人类反馈将合成医学图像与临床知识对齐

Aligning Synthetic Medical Images with Clinical Knowledge using Human  Feedback

The Impression section of a radiology report summarizes crucial radiology
findings in natural language and plays a central role in communicating these
findings to physicians. However, the process of generating impressions by
summarizing findings is time-consuming for radiologists and prone to errors. We
propose to automate the generation of radiology impressions with neural
sequence-to-sequence learning. We further propose a customized neural model for
this task which learns to encode the study background information and use this
information to guide the decoding process. On a large dataset of radiology
reports collected from actual hospital studies, our model outperforms existing
non-neural and neural baselines under the ROUGE metrics. In a blind experiment,
a board-certified radiologist indicated that 67% of sampled system summaries
are at least as good as the corresponding human-written summaries, suggesting
significant clinical validity. To our knowledge our work represents the first
attempt in this direction.

利用神经序列到序列学习自动化生成放射学诊断报告中印象部分，比现有基线效果更好，在盲测实验中 67% 样本的系统汇总得分至少和人类汇总得分一样高。