  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.
