5 papers · 1 filter
Weakly Supervised Multicenter Nancy Index Scoring in Ulcerative Colitis Using Foundation Models
Adam Kukučka, Ondřej Fabián, Vít Musil +1
Histologic assessment of ulcerative colitis (UC) activity is an important endpoint in clinical trials and routine care, but manual grading with indices such as the Nancy histologic…
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…
Beyond Occlusion: In Search for Near Real-Time Explainability of CNN-Based Prostate Cancer Classification
Martin Krebs, Jan Obdržálek, Vít Musil +1
Deep neural networks are starting to show their worth in critical applications such as assisted cancer diagnosis. However, for their outputs to get accepted in practice, the result…
LLEXICORP: End-user Explainability of Convolutional Neural Networks
Vojtěch Kůr, Adam Bajger, Adam Kukučka +3
Convolutional neural networks (CNNs) underpin many modern computer vision systems. With applications ranging from common to critical areas, a need to explain and understand the mod…
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…