BR-ChromNet: Banding resolution localization of chromosome structural abnormality with conditional random field.

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.