Deep learning approach to description and classification of fungi microscopic images. - Bartosz Zielinski 001; Agnieszka Sroka-Oleksiak; Dawid Rymarczyk; Adam Piekarczyk; Monika Brzychczy-Wloch (CoRR, 2019)

Deep learning approach to describe and classify fungi microscopic images. - Bartosz Zielinski 001; Agnieszka Sroka-Oleksiak; Dawid Rymarczyk; Adam Piekarczyk; Monika Brzychczy-Wloch (CoRR, 2020)

Kernel Self-Attention in Deep Multiple Instance Learning. - Dawid Rymarczyk; Jacek Tabor; Bartosz Zielinski 001 (CoRR, 2020)

ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery. - Dawid Rymarczyk; Lukasz Struski; Jacek Tabor; Bartosz Zielinski 001 (CoRR, 2020)

Classifying bacteria clones using attention-based deep multiple instance learning interpreted by persistence homology. - Adriana Borowa; Dawid Rymarczyk; Dorota Ochonska; Monika Brzychczy-Wloch; Bartosz Zielinski 001 (CoRR, 2020)

Deep learning classification of bacteria clones explained by persistence homology. - Adriana Borowa; Dawid Rymarczyk; Dorota Ochonska; Monika Brzychczy-Wloch; Bartosz Zielinski 001 (IJCNN, 2021)

ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classification. - Dawid Rymarczyk; Lukasz Struski; Jacek Tabor; Bartosz Zielinski 001 (KDD, 2021)

Comparison of Supervised and Self-Supervised Deep Representations Trained on Histological Images. - Dawid Rymarczyk; Adriana Borowa; Anna Bracha; Maurycy Chronowski; Wojciech Ozimek; Bartosz Zielinski 001 (MedInfo, 2021)

Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning. - Dawid Rymarczyk; Adriana Borowa; Jacek Tabor; Bartosz Zielinski 001 (WACV, 2021)

ProtoMIL: Multiple Instance Learning with Prototypical Parts for Fine-Grained Interpretability. - Dawid Rymarczyk; Aneta Kaczynska; Jaroslaw Kraus; Adam Pardyl; Bartosz Zielinski 001 (CoRR, 2021)

Interpretable Image Classification with Differentiable Prototypes Assignment. - Dawid Rymarczyk; Lukasz Struski; Michal Górszczak; Koryna Lewandowska; Jacek Tabor; Bartosz Zielinski 001 (ECCV (12), 2022)

Automating Patient-Level Lung Cancer Diagnosis in Different Data Regimes. - Adam Pardyl; Dawid Rymarczyk; Zbislaw Tabor; Bartosz Zielinski 001 (ICONIP (7), 2022)

ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification. - Dawid Rymarczyk; Adam Pardyl; Jaroslaw Kraus; Aneta Kaczynska; Marek Skomorowski; Bartosz Zielinski 001 (ECML/PKDD (1), 2022)

CompLung: Comprehensive Computer-Aided Diagnosis of Lung Cancer. - Adam Pardyl; Dawid Rymarczyk; Joanna Jaworek-Korjakowska; Dariusz Kucharski; Andrzej Brodzicki; Julia Lasek; Zofia Schneider; Iwona Kucybala; Andrzej Urbanik; Rafal Obuchowicz; Zbislaw Tabor; Bartosz Zielinski 001 (ECAI, 2023)

ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging. - Lukasz Struski; Dawid Rymarczyk; Arkadiusz Lewicki; Robert Sabiniewicz; Jacek Tabor; Bartosz Zielinski 001 (ECAI, 2023)

ICICLE: Interpretable Class Incremental Continual Learning. - Dawid Rymarczyk; Joost van de Weijer 001; Bartosz Zielinski 001; Bartlomiej Twardowski (ICCV, 2023)

ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction. - Dawid Rymarczyk; Daniel Dobrowolski; Tomasz Danel (SDM, 2023)

ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts. - Mikolaj Sacha; Dawid Rymarczyk; Lukasz Struski; Jacek Tabor; Bartosz Zielinski 001 (WACV, 2023)

Token Recycling for Efficient Sequential Inference with Vision Transformers. - Jan Olszewski; Dawid Rymarczyk; Piotr Wójcik; Mateusz Pach; Bartosz Zielinski 001 (CoRR, 2023)

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning. - Adriana Borowa; Dawid Rymarczyk; Dorota Ochonska; Agnieszka Sroka-Oleksiak; Monika Brzychczy-Wloch; Bartosz Zielinski 001 (IEEE J. Biomed. Health Informatics, 2023)

Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations. - Mikolaj Sacha; Bartosz Jura; Dawid Rymarczyk; Lukasz Struski; Jacek Tabor; Bartosz Zielinski 001 (AAAI, 2024)

ProtoNCD: Prototypical Parts for Interpretable Novel Class Discovery. - Tomasz Michalski 002; Dawid Rymarczyk; Daniel Barczyk; Bartosz Zielinski 001 (ESANN, 2024)

Revisiting FunnyBirds Evaluation Framework for Prototypical Parts Networks. - Szymon Oplatek; Dawid Rymarczyk; Bartosz Zielinski 001 (xAI (1), 2024)

OMENN: One Matrix to Explain Neural Networks. - Adam Wróbel; Mikolaj Janusz; Bartosz Zielinski 001; Dawid Rymarczyk (CoRR, 2024)

HCS-DFC: A Diffusion Classifier for Mode of Action Prediction Using Morphological Profiles. - Jakub Kosciukiewicz; Dawid Rymarczyk; Bartosz Zielinski 001 (CVPR Workshops, 2025)

LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision. - Mateusz Pach; Koryna Lewandowska; Jacek Tabor; Bartosz Michal Zielinski; Dawid Damian Rymarczyk (ICLR, 2025)

TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration. - Jan Olszewski; Dawid Rymarczyk; Piotr Wójcik; Mateusz Pach; Bartosz Zielinski 001 (WACV, 2025)

SEMU: Singular Value Decomposition for Efficient Machine Unlearning. - Marcin Sendera; Lukasz Struski; Kamil Ksiazek; Kryspin Musiol; Jacek Tabor; Dawid Rymarczyk (CoRR, 2025)

AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients. - Agnieszka Sroka-Oleksiak; Adam Pardyl; Dawid Rymarczyk; Aldona Olechowska-Jarzab; Katarzyna Biegun-Drozdz; Dorota Ochonska; Michal Wronka; Adriana Borowa; Tomasz Gosiewski; Milosz Adamczyk; Henryk Telega; Bartosz Zielinski 001; et al. (CoRR, 2025)

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data. - Magdalena Proszewska; Tomasz Danel; Dawid Rymarczyk (CoRR, 2025)

Personalized Interpretability - Interactive Alignment of Prototypical Parts Networks. - Tomasz Michalski 002; Adam Wróbel; Andrea Bontempelli; Jakub Lustyk; Mikolaj Kniejski; Stefano Teso; Andrea Passerini; Bartosz Zielinski 001; Dawid Rymarczyk (CoRR, 2025)

SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence. - Viktar Dubovik; Lukasz Struski; Jacek Tabor; Dawid Rymarczyk (CoRR, 2025)