Automatic Chromosome Classification using Deep Attention Based Sequence Learning of Chromosome Bands

Abstract

Chromosome Karyotyping refers to the task of segmenting and classifying individual chromosome images obtained from stained cell images microphotographed during the metaphase stage of cell division. The karyotyped images are useful for diagnosis of genetic disorders such as down syndrome, turner syndrome and certain types of cancers. In many hospitals and labs, a significant amount of manual effort and time is spent on segmenting and classifying the individual chromosome images. Recently, deep learning models have been applied to automate this task with promising results. An important characteristic of a chromosome is the presence of sequence of dark and light bands produced by giemsa staining which is used by cytogeneticists to manually perform karyotyping. We propose Residual Convolutional Recurrent Attention Neural Network (Res-CRANN) which exploits this property of band sequence for chromo-some classification. Res-CRANN is end-to-end trainable in which a sequence of feature vectors, extracted from the feature maps produced by convolutional layers of Residual neural networks (ResNet) is fed into Recurrent Neural Networks (RNN) and subsequently, an attention mechanism is applied on top of RNN output sequences which are further classified into one of the 24 labels. The attention mechanism after recurrent layers facilitates the network to learn to pay selective attention to the sequence of bands and relate them to different classes of chromosomes. We demonstrate the proposed architecture’s efficacy on a publicly available Bioimage chromosome classification dataset and observe that our model outperforms the baseline models created using traditional deep convolutional neural network and ResNet-50 by approximately 3% Top-1 classification accuracy.