3 papers
cs.CV2025
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…
cs.CV2025
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…
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…