Abstract
Bioimage classification plays a crucial role in many biological problems. Here we present a new General Purpose (GenP) ensemble that boosts performance by combining local features, dense sampling features, and deep learning approaches. We propose an ensemble of deep learning methods built using different criteria (different batch sizes, learning rates, topologies, and data augmentation methods). One of the contributions of this paper is the proposal of new methods of data augmentation based on feature transforms (principal component analysis/discrete cosine transform) that boost performance of Convolutional Neural Networks (CNNs). Each handcrafted descriptor is used to train a different Support Vector Machine (SVM), and the different SVMs are combined with the ensemble of CNNs. Our method is evaluated on a diverse set of bioimage classification problems. Results demonstrate that the proposed GenP bioimage ensemble obtains state-of-the-art performance without any ad-hoc dataset tuning of parameters (avoiding the risk of overfitting/overtraining).
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URL
http://arxiv.org/abs/1904.08084