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.
@article{arxiv.2403.11073,
title = {Tokensome: Towards a Genetic Vision-Language GPT for Explainable and Cognitive Karyotyping},
author = {Haoxi Zhang and Xinxu Zhang and Yuanxin Lin and Maiqi Wang and Yi Lai and Yu Wang and Linfeng Yu and Yufeng Xu and Ran Cheng and Edward Szczerbicki},
journal= {arXiv preprint arXiv:2403.11073},
year = {2024}
}