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.CV2025
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…
cs.PL2024
Minuska: Towards a Formally Verified Programming Language Framework
Jan Tušil, Jan Obdržálek
Programming language frameworks allow us to generate language tools (e.g., interpreters) just from a formal description of the syntax and semantics of a programming language. As th…