Varifocal-Net: A Chromosome Classification Approach using Deep Convolutional Networks

TL;DR

Varifocal-Net is a deep learning approach that simultaneously classifies chromosome type and polarity by combining global and local features through a varifocal mechanism, significantly improving accuracy in karyotyping.

Contribution

The paper introduces a novel varifocal mechanism and multi-scale feature ensemble for chromosome classification, outperforming existing methods in accuracy.

Findings

Achieved 99.2% accuracy in patient case classification.

Outperformed state-of-the-art methods in chromosome classification.

Effective use of multi-scale features and dispatch strategy.

Abstract

Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosome's type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one local-scale network (L-Net). It follows three stages. The first stage is to learn both global and local features. We extract global features and detect finer local regions via the G-Net. By proposing a varifocal mechanism, we zoom into local parts and extract local features via the L-Net. Residual learning and multi-task learning strategies are utilized to promote high-level feature extraction. The detection of discriminative local parts is fulfilled by a localization subnet of the G-Net, whose training process involves both supervised and weakly-supervised learning. The second stage is to build two multi-layer perceptron classifiers that exploit features of both two scales to boost classification performance. The third stage is to introduce a dispatch strategy of assigning each chromosome to a type within each patient case, by utilizing the domain knowledge of karyotyping. Evaluation results from 1909 karyotyping cases showed that the proposed Varifocal-Net achieved the highest accuracy per patient case (%) 99.2 for both type and polarity tasks. It outperformed state-of-the-art methods, demonstrating the effectiveness of our varifocal mechanism, multi-scale feature ensemble, and dispatch strategy. The proposed method has been applied to assist practical karyotype diagnosis.