论文标题
核型AI用于精确肿瘤学
Karyotype AI for Precision Oncology
论文作者
论文摘要
我们提出了一种能够准确检测到直接从细胞分裂中期显微镜图像引起血液癌的染色体异常的机器学习方法。该管道建立在一系列微调的视觉变压器上。当前的最新技术（和标准临床实践）需要昂贵的手动专家分析，而我们的管道只需15秒钟的海平面图像。使用一种新颖的预处理策略来减轻数据稀缺性的挑战，我们为临床上具有重要意义的DEL（5Q）和T（9; 22）异常获得了高精度核心评分为94％的AUC。我们的方法还可以根据模型潜在嵌入来解锁对罕见畸变的零射击检测。直接从中期图像中快速，准确，可靠地诊断出遗传异常的能力可以改变核分型实践并改善患者预后。我们将使代码公开可用。
We present a machine learning method capable of accurately detecting chromosome abnormalities that cause blood cancers directly from microscope images of the metaphase stage of cell division. The pipeline is built on a series of fine-tuned Vision Transformers. Current state of the art (and standard clinical practice) requires expensive, manual expert analysis, whereas our pipeline takes only 15 seconds per metaphase image. Using a novel pretraining-finetuning strategy to mitigate the challenge of data scarcity, we achieve a high precision-recall score of 94% AUC for the clinically significant del(5q) and t(9;22) anomalies. Our method also unlocks zero-shot detection of rare aberrations based on model latent embeddings. The ability to quickly, accurately, and scalably diagnose genetic abnormalities directly from metaphase images could transform karyotyping practice and improve patient outcomes. We will make code publicly available.
