  Bioimage classification plays a crucial role in many biological problems. In
this work, we present a new General Purpose (GenP) ensemble that boosts
performance by combining local features, dense sampling features, and deep
learning approaches. First, we introduce three new methods for data
augmentation based on PCA/DCT; second, we show that different data augmentation
approaches can boost the performance of an ensemble of CNNs; and, finally, we
propose 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. 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
(thus avoiding the risk of overfitting/overtraining).
