DiagSet: a dataset for prostate cancer histopathological image classification.

Cancer diseases constitute one of the most significant societal challenges. In this paper, we introduce a novel histopathological dataset for prostate cancer detection. The proposed dataset, consisting of over 2.

An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals.

One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves polysomnography examination, which is expensive.

Thumbnail Tensor-A Method for Multidimensional Data Streams Clustering with an Efficient Tensor Subspace Model in the Scale-Space.

In this paper an efficient method for signal change detection in multidimensional data streams is proposed. A novel tensor model is suggested for input signal representation and analysis. The model is built from a part of the multidimensional stream by construction of the representing orthogonal tensor subspaces, computed with the higher-order singular value decomposition (HOSVD).

Speckle Noise Filtering in Side-Scan Sonar Images Based on the Tucker Tensor Decomposition.

Real signals are usually contaminated with various types of noise. This phenomenon has a negative impact on the operation of systems that rely on signals processing. In this paper, we propose a tensor-based method for speckle noise reduction in the side-scan sonar images.

Dental implant examination based on the log-polar matching of the maxillary radiograph images in the anisotropic scale space.

In this paper a computer vision system is proposed for automatic examination of implant placements based on the maxillary radiograph images. To find rotated and scale changed implants the system does template matching in the extended log-polar space. Matching is proposed to be performed in the anisotropic scale-space, starting from the coarse level.

Color image segmentation with support vector machines: applications to road signs detection.

In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the road signs recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space.