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Bioimage classification plays a crucial role in many biological problems. Inthis work, we present a new General Purpose (GenP) ensemble that boostsperformance by combining local features, dense sampling features, and deeplearning approaches. First, we introduce three new methods for dataaugmentation based on PCA/DCT; second, we show that different data augmentationapproaches can boost the performance of an ensemble of CNNs; and, finally, wepropose a set of handcrafted/learned descriptors that are highly generalizable.Each handcrafted descriptor is used to train a different Support Vector Machine(SVM), and the different SVMs are combined with the ensemble of CNNs. Ourmethod is evaluated on a diverse set of bioimage classification problems.Results demonstrate that the proposed GenP bioimage ensemble obtainsstate-of-the-art performance without any ad-hoc dataset tuning of parameters(thus avoiding the risk of overfitting/overtraining).

General Purpose (GenP) Bioimage Ensemble of Handcrafted and Learned Features with Data Augmentation