5 papers
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…
Weakly Supervised Multicenter Nancy Index Scoring in Ulcerative Colitis Using Foundation Models
Adam KukuÄka, OndÅej Fabián, VÃt 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…
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…
Beyond Occlusion: In Search for Near Real-Time Explainability of CNN-Based Prostate Cancer Classification
Martin Krebs, Jan Obdržálek, VÃt 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
VojtÄch Kůr, Adam Bajger, Adam KukuÄka +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…