  Chromosome classification is an important but difficult and tedious task in
karyotyping. Previous methods only classify manually segmented single
chromosome, which is far from clinical practice. In this work, we propose a
detection based method, DeepACC, to locate and fine classify chromosomes
simultaneously based on the whole metaphase image. We firstly introduce the
Additive Angular Margin Loss to enhance the discriminative power of model. To
alleviate batch effects, we transform decision boundary of each class
case-by-case through a siamese network which make full use of prior knowledges
that chromosomes usually appear in pairs. Furthermore, we take the clinically
seven group criterion as a prior knowledge and design an additional Group
Inner-Adjacency Loss to further reduce inter-class similarities. 3390 metaphase
images from clinical laboratory are collected and labelled to evaluate the
performance. Results show that the new design brings encouraging performance
gains comparing to the state-of-the-art baselines.
