3 papers
cs.CV2026
Explaining Digital Pathology Models via Clustering Activations
Adam Bajger, Jan Obdržálek, VojtÄch 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.CV2026
LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images
MatÄj Pekár, VÃt Musil, Rudolf Nenutil +2
Precise and scalable instance segmentation of cell nuclei is essential for computational pathology, yet gigapixel Whole-Slide Images pose major computational challenges. Existing a…
cs.CL2025
Evaluating Open-Weight Large Language Models for Structured Data Extraction from Narrative Medical Reports Across Multiple Use Cases and Languages
Douwe J. Spaanderman, Karthik Prathaban, Petr Zelina +20
Large language models (LLMs) are increasingly used to extract structured information from free-text clinical records, but prior work often focuses on single tasks, limited models,…