DEEPACC:Automate Chromosome Classification Based On Metaphase Images Using Deep Learning Framework Fused With Priori Knowledge

Abstract

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 the model. To alleviate batch effects, we transform decision boundary of each class case-by-case through a siamese network which make full use of priori knowledges that chromosomes usually appear in pairs. Furthermore, we take the clinically seven group criteria as a prior-knowledge and design an additional Group Inner-Adjacency Loss to further reduce inter-class similarities. A private metaphase image dataset 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 baseline models.