文献类型：期刊文献

英文题名：Radiology Report Generation via Multi-Objective Preference Optimization

作者：Xiao, Ting[1]; Shi, Lei[1]; Liu, Peng[2]; Wang, Zhe[1]; Bai, Chenjia[3]

机构：[1] East China University of Science and Technology, China; [2] Harbin Institute of Technology, China; [3] Institute of Artificial Intelligence [TeleAI], China Telecom, China

年份：2025

卷号：39

期号：8

起止页码：8664

外文期刊名：Proceedings of the AAAI Conference on Artificial Intelligence

收录：EI(收录号：20251918376140)

语种：英文

外文关键词：Support vector regression - Vector spaces

摘要：Automatic Radiology Report Generation (RRG) is an important topic for alleviating the substantial workload of radiologists. Existing RRG approaches rely on supervised regression based on different architectures or additional knowledge injection, while the generated report may not align optimally with radiologists' preferences. Especially, since the preferences of radiologists are inherently heterogeneous and multidimensional, e.g., some may prioritize report fluency, while others emphasize clinical accuracy. To address this problem, we propose a new RRG method via Multi-objective Preference Optimization (MPO) to align the pre-trained RRG model with multiple human preferences, which can be formulated by multi-dimensional reward functions and optimized by multi-objective reinforcement learning (RL). Specifically, we use a preference vector to represent the weight of preferences and use it as a condition for the RRG model. Then, a linearly weighed reward is obtained via a dot product between the preference vector and multi-dimensional reward. Next, the RRG model is optimized to align with the preference vector by optimizing such a reward via RL. In the training stage, we randomly sample diverse preference vectors from the preference space and align the model by optimizing the weighted multi-objective rewards, which leads to an optimal policy on the entire preference space. When inference, our model can generate reports aligned with specific preferences without further fine-tuning. Extensive experiments on two public datasets show the proposed method can generate reports that cater to different preferences in a single model and achieve state-of-the-art performance. Copyright ? 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.