Do not repeat these mistakes - a critical appraisal of applications of explainable artificial intelligence for image based COVID-19 detection. - Weronika Hryniewska; Przemyslaw Bombinski; Patryk Szatkowski; Paulina Tomaszewska; Artur Przelaskowski; Przemyslaw Biecek (CoRR, 2020)

Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies. - Weronika Hryniewska; Przemyslaw Bombinski; Patryk Szatkowski; Paulina Tomaszewska; Artur Przelaskowski; Przemyslaw Biecek (Pattern Recognit., 2021)

Duel-based Deep Learning system for solving IQ tests. - Paulina Tomaszewska; Adam Zychowski; Jacek Mandziuk (AISTATS, 2022)

Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift. - Paulina Tomaszewska; Christoph H. Lampert (ICPR, 2022)

Information Injection to Deep Learning Solutions in Knowledge Transfer. - Paulina Tomaszewska (IJCAI, 2022)

Promoting Reproducibility of Research Results in International Events (Report from the 4th RRPR). - Bertrand Kerautret; Konstantin Kirchheim; Daniel P. Lopresti; Phuc Ngo 001; Paulina Tomaszewska (RRPR, 2022)

On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) Under Class-Prior Shift. - Paulina Tomaszewska; Christoph H. Lampert (RRPR, 2022)

Position: Do Not Explain Vision Models Without Context. - Paulina Tomaszewska; Przemyslaw Biecek (ICML, 2024)

Deep spatial context: when attention-based models meet spatial regression. - Paulina Tomaszewska; Elzbieta Sienkiewicz; Mai P. Hoang; Przemyslaw Biecek (CoRR, 2024)

Position paper: Do not explain (vision models) without context. - Paulina Tomaszewska; Przemyslaw Biecek (CoRR, 2024)

Does context matter in digital pathology? - Paulina Tomaszewska; Mateusz Sperkowski; Przemyslaw Biecek (CoRR, 2024)

Deep Spatial Context: When Attention-Based Models Meet Spatial Regression (Digital Pathology). - Paulina Tomaszewska; Elzbieta Sienkiewicz; Mai P. Hoang; Przemyslaw Biecek (AIME (1), 2025)