activity
20202026
most citedLEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections

3 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images

Ivica Kopriva, Dario Sitnik, Arijana Pacic +3

As hematoxylin & eosin (H&E) staining constitutes the primary entry point in routine diagnostic workflows, computer-aided diagnosis from whole-slide H&E images is of particular cli…

eess.IV2024★ 1 cited

A Hyperspectral Imaging Dataset and Methodology for Intraoperative Pixel-Wise Classification of Metastatic Colon Cancer in the Liver

Ivica Kopriva, Dario Sitnik, Laura-Isabelle Dion-Bertrand +3

Hyperspectral imaging (HSI) holds significant potential for transforming the field of computational pathology. However, there is currently a shortage of pixel-wise annotated HSI da…

eess.IV2022★ 3 cited

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…

eess.IV2022

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…

cs.CV2020★ 1 cited

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…