Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images
This work addresses a domain-specific problem for medical diagnosis by improving classification accuracy for glioma cell nuclei, representing an incremental advancement in CNN-based methods for pathology image analysis.
The paper tackled automated classification of glioma nuclear shapes and attributes in pathology images by proposing three methods to enhance a semi-supervised CNN, resulting in error rate reductions of 21.54% for attributes and 15.07% for shapes compared to the state-of-the-art.
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.