# Tokensome: Towards a Genetic Vision-Language GPT for Explainable and Cognitive Karyotyping

arXiv: https://arxiv.org/abs/2403.11073 · HTML: https://mlsanity.com/paper/2403.11073/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Self-Supervised Learning of Object Parts for Semantic Segmentation](https://mlsanity.com/paper/2204.13101/) (2022)
- [Subobject-level Image Tokenization](https://mlsanity.com/paper/2402.14327/) (2024)
- [Varifocal-Net: A Chromosome Classification Approach using Deep Convolutional Networks](https://mlsanity.com/paper/1810.05943/) (2018)
- [Supervised Contrastive Learning for Fine-grained Chromosome Recognition](https://mlsanity.com/paper/2312.07623/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Systematic Review on the Generative AI Applications in Human Medical Genomics](https://mlsanity.com/paper/2508.20275/) (2025)
- [ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics](https://mlsanity.com/paper/2603.11872/) (2026)

## Abstract (arXiv)

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.