  Pathogenic chromosome abnormalities are very common among the general
population. While numerical chromosome abnormalities can be quickly and
precisely detected, structural chromosome abnormalities are far more complex
and typically require considerable efforts by human experts for identification.
This paper focuses on investigating the modeling of chromosome features and the
identification of chromosomes with structural abnormalities. Most existing
data-driven methods concentrate on a single chromosome and consider each
chromosome independently, overlooking the crucial aspect of homologous
chromosomes. In normal cases, homologous chromosomes share identical
structures, with the exception that one of them is abnormal. Therefore, we
propose an adaptive method to align homologous chromosomes and diagnose
structural abnormalities through homologous similarity. Inspired by the process
of human expert diagnosis, we incorporate information from multiple pairs of
homologous chromosomes simultaneously, aiming to reduce noise disturbance and
improve prediction performance. Extensive experiments on real-world datasets
validate the effectiveness of our model compared to baselines.
