output
20222026
most citedUnsupervised extraction, labelling and clustering of segments from clinical notes

5 citations

5 papers

cs.LG2026

Computing patient similarity based on unstructured clinical notes

Petr Zelina, Marko Řeháček, Jana Halámková +3

Clinical notes hold rich yet unstructured details about diagnoses, treatments, and outcomes that are vital to precision medicine but hard to exploit at scale. We introduce a method…

cs.CV2026

LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images

Matěj Pekár, Vít Musil, Rudolf Nenutil +2

Background and Objective: Precise and scalable instance segmentation of cell nuclei is a fundamental prerequisite for computational pathology, yet gigapixel whole-slide images (WSI…

cs.CV2025

Explaining Digital Pathology Models via Clustering Activations

Adam Bajger, Jan Obdržálek, Vojtěch Kůr +4

We present a clustering-based explainability technique for digital pathology models based on convolutional neural networks. Unlike commonly used methods based on saliency maps, suc…

cs.CV2025★ 1 cited

From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology

Gernot Fiala, Markus Plass, Robert Harb +15

A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissue sections or cell samples, at…

cs.CL2022★ 5 cited

Unsupervised extraction, labelling and clustering of segments from clinical notes

Petr Zelina, Jana Halámková, Vít Nováček

This work is motivated by the scarcity of tools for accurate, unsupervised information extraction from unstructured clinical notes in computationally underrepresented languages, su…