2 citations · 2 across the 3 of their papers we have counts for
3 papers
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…
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,…
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…