  Classifying the various shapes and attributes of a glioma cell nucleus is
crucial for diagnosis and understanding the disease. We investigate automated
classification of glioma nuclear shapes and visual attributes using
Convolutional Neural Networks (CNNs) on pathology images of automatically
segmented nuclei. We propose three methods that improve the performance of a
previously-developed semi-supervised CNN. First, we propose a method that
allows the CNN to focus on the most important part of an image- the image's
center containing the nucleus. Second, we inject (concatenate) pre-extracted
VGG features into an intermediate layer of our Semi-Supervised CNN so that
during training, the CNN can learn a set of complementary features. Third, we
separate the losses of the two groups of target classes (nuclear shapes and
attributes) into a single-label loss and a multi-label loss so that the prior
knowledge of inter-label exclusiveness can be incorporated. On a dataset of
2078 images, the proposed methods combined reduce the error rate of attribute
and shape classification by 21.54% and 15.07% respectively compared to the
existing state-of-the-art method on the same dataset.
