Harnessing Few-Shot Learning Segmentation for Histopathology: A Comprehensive Practical Study
Joanna I. Szołomicka, Urszula Markowska-Kaczmar
Intelligent systems reference library
2026
Profil
Urszula Markowska-Kaczmar
Profesor zwyczajny
Grupy badawcze
Wykształcenie
1976
magister informatyki
Politechnika Wrocławska
1980
doktor informatyki, sztuczna inteligencja
Politechnika Wrocławska
2008
doktor habilitowany w dyscyplinie informatyka
Instytut Badań Systemowych Polskiej Akademii Nauk
2021
profesor w dyscyplinie informatyka techniczna i telekomunikacja
Politechnika Wrocławska
Publikacje(152)
Exploring action selection methods in user-driven image generation
Daniil Hardzetski, Urszula Markowska-Kaczmar
Communications in computer and information science
2026
Cross-domain few-shot segmentation of histopathology images with Mamba
Joanna I. Szołomicka, Urszula Markowska-Kaczmar
Cham : Springer Nature Switzerland AG, cop. 2026. s. 228–241.
2026
MAGGNet: guided volumetric metal artefact synthesis for computed tomography scans
Jakub Drzewiecki, Igor Hołowacz, Bartosz Lewandowski, Filip Fit, Urszula Markowska-Kaczmar
Cham : Springer Nature Switzerland AG, cop. 2026. s. 346-353.
2026
Masticatory organ dysfunction recognition based on multimodal information
Urszula Markowska-Kaczmar, Michał Wawrzyniak, Karol Szymończyk, Joanna I Szołomicka, Jakub Pyka, Karol Kulawiec, Michał Karolewski, Aneta Demidaś
Engineering Applications of Artificial Intelligence 2025 vol. 144, art. 109998, s. 1-20.
2025
Deep learning for cancer cell detection: do we need dedicated models?
Michał P. Karol, Martin Tabakow, Urszula Markowska-Kaczmar, Łukasz Fuławka
Artificial Intelligence Review 2024 vol. 57, art. 53, s. 1-36.
2024
Gradient Overdrive: aviding negative randomness effects in Stochastic Gradient Descent
Urszula Markowska-Kaczmar, Filip Strzałka
Singapore : Springer, cop. 2024. s. 175-186.
2024
Advances in smart healthcare paradigms and applications : outstanding women in healthcare
Halina Kwaśnicka, Urszula Markowska-Kaczmar, Nikhil Jain, Chee Peng Lim, Lakhmi C Jain
Intelligent systems reference library
2023
An overview of few-shot learning methods in analysis of histopathological images
Joanna I. Szołomicka, Urszula Markowska-Kaczmar
Intelligent Systems Reference Library
2023
Semi‑supervised classifier guided by discriminator
Sebastian Jamroziński, Urszula Markowska-Kaczmar
Scientific Reports 2022 vol. 12, art. 14665, s. 1-17.
2022
Extreme learning machine versus classical feedforward network
Urszula Markowska-Kaczmar, Michał Kosturek
Neural Computing and Applications
2021
Utterance style transfer using deep models
Daniel Popek, Urszula Markowska-Kaczmar
Procedia Computer Science
2021
Creating herd behavior by virtual agents using neural networks
Urszula Markowska-Kaczmar, Adrian Ślimak
Procedia Computer Science
2021
Capsule network versus convolutional neural network in image classification
Ewa Juralewicz, Urszula Markowska-Kaczmar
Lecture notes in computer science
2021
Interpreting neural networks prediction for a single instance via random forest feature contributions
Urszula Markowska-Kaczmar, Anna Palczewska
Lecture notes in computer science
2021
Human flow recognition using deep learning and image processing methods
Mateusz Zimoch, Urszula Markowska-Kaczmar
Engineering Applications of Artificial Intelligence
2021
Markov network versus recurrent neural network in forming herd behavior based on sight and simple sound communication
Urszula Markowska-Kaczmar, Tomasz Marcinkowski
Applied Soft Computing
2020
Inference ability assessment of modified Differential Neural Computer
Urszula Markowska-Kaczmar, Grzegorz Kułakowski
Lecture notes in computer science
2020
Computational methods for resting-state EEG of patients with disorders of consciousness
Urszula Markowska-Kaczmar, Silvia Corchs, Giovanni Chioma, Riccardo Dondi, Francesca Gasparini, Sara Manzoni, Giancarlo Mauri, Italo Zoppis, Angela Morreale
Frontiers in Neuroscience 2019 vol. 13, art. 807, s. 1-7.
2019
Wyświetlono 20 z 152