490 citations · 590 across the 6 of their papers we have counts for
6 papers
LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections
Dario Sitnik, Ivica Kopriva
Accurate segmentation of medical images is essential for diagnosis and treatment of diseases. These problems are solved by highly complex models, such as deep networks (DN), requir…
Clustering and classification of low-dimensional data in explicit feature map domain: intraoperative pixel-wise diagnosis of adenocarcinoma of a colon in a liver
Dario Sitnik, Ivica Kopriva
Application of artificial intelligence in medicine brings in highly accurate predictions achieved by complex models, the reasoning of which is hard to interpret. Their generalizati…
Low Tensor Train- and Low Multilinear Rank Approximations for De-speckling and Compression of 3D Optical Coherence Tomography Images
Ivica Kopriva, Fei Shi, Mingying Lai +3
This paper proposes low tensor-train (TT) rank and low multilinear (ML) rank approximations for de-speckling and compression of 3D optical coherence tomography (OCT) images for a g…
Robust Self-Supervised Convolutional Neural Network for Subspace Clustering and Classification
Dario Sitnik, Ivica Kopriva
Insufficient capability of existing subspace clustering methods to handle data coming from nonlinear manifolds, data corruptions, and out-of-sample data hinders their applicability…
-Motivated Low-Rank Sparse Subspace Clustering
Maria Brbić, Ivica Kopriva
In many applications, high-dimensional data points can be well represented by low-dimensional subspaces. To identify the subspaces, it is important to capture a global and local st…
Multi-view Low-rank Sparse Subspace Clustering
Maria Brbic, Ivica Kopriva
Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral cl…