DeepACC:Automate Chromosome Classification based on Metaphase Images using Deep Learning Framework Fused with Prior Knowledge

TL;DR

DeepACC is a deep learning framework that automates chromosome detection and classification from metaphase images, incorporating prior knowledge and novel loss functions to improve accuracy in clinical karyotyping.

Contribution

The paper introduces DeepACC, a novel detection-based method that combines deep learning with prior knowledge and specialized loss functions for chromosome classification.

Findings

Achieved improved classification accuracy over state-of-the-art methods.

Effectively utilizes prior knowledge of chromosome pairing and grouping.

Demonstrated robustness on 3390 clinical metaphase images.

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 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.