Abstract：
Detecting chromosome structural abnormalities in medical genetics is essential for diagnosing genetic disorders and understanding their implications for an individual\'s health. However, existing computational methods are formulated as a binary-class classification problem trained only on representations of positive/negative chromosome pairs. This paper introduces an innovative framework for detecting chromosome abnormalities with banding resolution, capable of precisely identifying and masking the specific abnormal regions. We highlight a pixel-level abnormal mapping strategy guided by banding features. This approach integrates data from both the original image and banding characteristics, enhancing the interpretability of prediction results for cytogeneticists. Furthermore, we have implemented an ensemble approach that pairs a discriminator with a conditional random field heatmap generator. This combination significantly reduces the false positive rate in abnormality screening. We benchmarked our proposed framework with state-of-the-art (SOTA) methods in abnormal screening and structural abnormal region segmentation. Our results show cutting-edge effectiveness and greatly reduce the high false positive rate. It also shows superior performance in sensitivity and segmentation accuracy. Being able to identify abnormal regions consistently shows that our model has demonstrated significant clinical utility with high model interpretability. BRChromNet is open-sourced and available at https://github.com/frankchen121212/BR-ChromNet.
摘要：
在医学遗传学中检测染色体结构异常对于诊断遗传疾病和了解其对个体健康的影响至关重要。然而,现有的计算方法被表述为仅在正/负染色体对的表示上训练的二进制类分类问题。本文介绍了一种具有条带分辨率的检测染色体异常的创新框架，能够精确识别和掩盖特定的异常区域。我们强调了一种以条带特征为指导的像素级异常映射策略。这种方法集成了来自原始图像和条带特征的数据，增强细胞遗传学家预测结果的可解释性。此外，我们已经实现了一种集成方法，该方法将鉴别器与条件随机场热图生成器配对。这种组合显著降低了异常筛查中的假阳性率。我们在异常筛选和结构异常区域分割中使用最先进的（SOTA）方法对我们提出的框架进行了基准测试。我们的结果显示了尖端的有效性，并大大降低了高误报率。它还在灵敏度和分割精度方面显示出优越的性能。能够识别异常区域一致地表明我们的模型已经证明了具有高模型可解释性的显著临床效用。BRChromNet是开源的，可在https://github.com/frankchen121212/BR-ChromNet上获得。
