AI自动识别分裂期染色体，秒级完成显微图像分析
Aycromo: An Open-Source Platform for Automatic Chromosome Detection in Metaphase Images Based on Deep Learning
- 基于YOLOv11的深度学习模型，支持图形化交互操作
- 在CRCN-NE数据集上达到99.40% mAP@50检测精度
- 开源桌面平台，适合临床医生快速辅助诊断
染色体分析是遗传病诊断的基础步骤，但传统人工核型分析耗时长且依赖专家，每例患者需数天。尽管深度学习在染色体检测中表现优异，多数方案仍局限于研究原型或缺乏适合临床使用的图形界面。本文提出Aycromo，一个基于Electron和ONNX Runtime的开源桌面平台，支持加载预训练模型、通过集成基准模块对比不同架构，并提供交互式标注界面实现手动修正，全程无需命令行操作。在CRCN-NE数据集上的初步实验表明，YOLOv11达到99.40% mAP@50，平台可将每张切片分析时间缩短至秒级。
原文摘要 · Abstract (English)
Chromosome analysis is a fundamental step in the diagnosis of genetic diseases, but the manual karyotyping workflow is time-consuming and heavily dependent on expert specialists, often requiring several days per patient. Although Deep Learning models have achieved high performance in chromosome detection, most proposed solutions remain restricted to research prototypes or lack graphical interfaces suitable for clinical use. In this work, we present Aycromo, an open-source desktop platform for AI-assisted cytogenetic analysis. Built on Electron and ONNX Runtime, the tool allows cytogeneticists to load pre-trained models, compare architectures through an integrated benchmarking module, and manually correct detections via an interactive annotation interface, all without command-line interaction. Preliminary experiments on metaphase images from the CRCN-NE dataset demonstrate that YOLOv11 achieves 99.40% mAP@50, while the platform reduces per-slide analysis to seconds
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。