arXiv cs.AI· Ryo Takizawa, Tempei Kabayama, Chiemi Yamazaki, Shun Kitamura, Ryo Matsuoka, Yuta Ando, Satoshi Kodera, Norihiko Takeda·· 3 小时前AI 评分26
基于监督式 LLM 在语言空间融合异构超声心动图 AI 输出
Integrating Heterogeneous Echocardiography AI Outputs in Language Space: Learnable Late Fusion with a Supervised LLM
AI 导读
研究者开发了基于监督式大语言模型（LLM）的后期融合模块，直接在语言空间聚合按视角生成的超声心动图预测所见陈述与测量值，无需将各模型输出转为二值决策、概率或固定长度特征向量。
正文 · 原文
Abstract:View- and target-specific echocardiography AI models capture useful but complementary information and may not achieve sufficient predictive performance in isolation. A learned ensemble is a natural way to combine these outputs, but heterogeneous models typically require each output to be converted into a binary decision, probability, confidence score, or fixed-length feature vector. We developed a supervised large language model (LLM)-based late-fusion module that directly aggregates view-specific predicted finding statements and predicted measurements in language space and learns a case-level fusion function. In patient-level three-fold cross-validation of 859 examinations from 646 patients, the proposed method integrated heterogeneous outputs within a single framework, performed comparably to the best-performing reference strategy evaluated, and outperformed a general-purpose LLM without task-specific training. These results suggest that a supervised LLM can combine multiple upstream models without model-specific output conversion or feature engineering that requires medical knowledge.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39464 [cs.CL] |
| (or arXiv:2610.39464v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39464 arXiv-issued DOI via DataCite |
Submission history
From: Ryo Takizawa [view email]
[v1]
Thu, 29 Oct 2026 06:36:39 UTC (606 KB)
来源：arXiv cs.AI · arxiv.org