Tokensome：面向可解释和认知性核型分析的遗传视觉-语言GPT
Tokensome: Towards a Genetic Vision-Language GPT for Explainable and Cognitive Karyotyping
AI总结：
本文提出Tokensome，一种基于染色体标记化的视觉-语言模型，通过知识图谱和LLMs将核型分析从视觉感知提升至认知决策层，增强可解释性并促进异常检测。
摘要：
自动核型分析通常被定义为仅关注染色体对象级建模的视觉感知任务。这一定义导致大多数现有方法忽略了成分性和整体性信息，显著限制了模型性能。此外，当前技术缺乏可解释性，阻碍了临床采用。在本文中，我们引入了Tokensome，一种基于染色体标记化的新型视觉-语言模型，用于可解释和认知性核型分析。Tokensome将方法从传统的视觉感知层提升至认知决策层。这一提升使得通过知识图谱和LLMs整合领域知识和认知推理成为可能，显著增强了模型的可解释性并促进了异常检测。
英文摘要：
Automatic karyotype analysis is often defined as a visual perception task focused solely on chromosomal object-level modeling. This definition has led most existing methods to overlook componential and holistic information, significantly constraining model performance. Moreover, the lack of interpretability in current technologies hinders clinical adoption. In this paper, we introduce Tokensome, a novel vision-language model based on chromosome tokenization for explainable and cognitive karyotyping. Tokensome elevates the method from the conventional visual perception layer to the cognitive decision-making layer. This elevation enables the integration of domain knowledge and cognitive reasoning via knowledge graphs and LLMs, markedly enhancing model's explainability and facilitating abnormality detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
