Abstract
Detecting structural chromosomal abnormalities is crucial for accurate diagnosis and management of genetic disorders. However, collecting sufficient structural abnormality data is extremely challenging and costly in clinical practice, and not all abnormal types can be readily collected. As a result, deep learning approaches face significant performance degradation due to the severe imbalance and scarcity of abnormal chromosome data. To address this challenge, we propose a Perturb-and-Restore (P&R), a simulation-driven structural augmentation framework that effectively alleviates data imbalance in chromosome anomaly detection. The P&R framework comprises two key components: (1) Structure Perturbation and Restoration Simulation, which generates synthetic abnormal chromosomes by perturbing chromosomal banding patterns of normal chromosomes followed by a restoration diffusion network that reconstructs continuous chromosome content and edges, thus eliminating reliance on rare abnormal samples; and (2) Energy-guided Adaptive Sampling, an energy score-based online selection strategy that dynamically prioritizes high-quality synthetic samples by referencing the energy distribution of real samples. To evaluate our method, we construct a comprehensive structural anomaly dataset consisting of over 260,000 chromosome images, including 4,242 abnormal samples spanning 24 categories. Experimental results demonstrate that the P&R framework achieves state-of-the-art (SOTA) performance, surpassing existing methods with an average improvement of 8.92% in sensitivity, 8.89% in precision, and 13.79% in F1-score across all categories.
Abstract (translated)
检测结构染色体异常对于遗传病的准确诊断和管理至关重要。然而，在临床实践中收集足够的结构异常数据极具挑战性且成本高昂，并非所有异常类型都能轻易获取。因此，由于异常染色体数据的严重不平衡和稀缺性，深度学习方法面临显著的性能下降。为应对这一挑战，我们提出了一种名为扰动-恢复（P&R）的模拟驱动结构增强框架，可有效缓解染色体异常检测中的数据不平衡问题。P&R框架包含两个关键组成部分：（1）结构扰动与恢复模拟，通过对正常染色体带型进行扰动生成合成异常染色体，并利用恢复扩散网络重建连续的染色体内容与边缘，从而消除对罕见异常样本的依赖；（2）能量引导的自适应采样，一种基于能量分数的在线选择策略，通过参考真实样本的能量分布，动态优先选择高质量合成样本。为评估该方法，我们构建了一个包含超过26万张染色体图像的综合性结构异常数据集，其中涵盖24个类别的4,242个异常样本。实验结果表明，P&R框架达到了最先进性能，在所有类别中分别以8.92%的灵敏度、8.89%的精确度和13.79%的F1分数超越了现有方法。
URL
https://arxiv.org/abs/2604.00854
