M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification

Data Augmentation for Plant Classification

Data augmentation plays a crucial role in increasing the number of training images, which often aids to improve classification performances of deep learning techniques for computer vision problems. In this paper, we employ the deep learning framework and determine the effects of several data-augmentation (DA) techniques for plant classification problems. For this, we use two convolutional neura...

Anatomical Data Augmentation For CNN based Pixel-wise Classification

In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset co...

M - Infosift : a Graph - Based Approach for Multiclass Document Classification

M-INFOSIFT: A GRAPH-BASED APPROACH FOR MULTICLASS DOCUMENT CLASSIFICATION

Structural and Graph-based Methods for Automatic Fingerprint Classification

Real Data Augmentation for Medical Image Classification

Many medical image classification tasks share a common unbalanced data problem. That is images of the target classes, e.g., certain types of diseases, only appear in a very small portion of the entire dataset. Nowadays, large co llections of medical images are readily available. However, it is costly and may not even be feasible for medical experts to manually comb through a huge unlabeled data...