Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Sobieski, Bartlomiej"'
Autor:
Sobieski, Bartlomiej, Grzywaczewski, Jakub, Sadlej, Bartlomiej, Tivnan, Matthew, Biecek, Przemyslaw
Visual counterfactual explanations (VCEs) have recently gained immense popularity as a tool for clarifying the decision-making process of image classifiers. This trend is largely motivated by what these explanations promise to deliver -- indicate sem
Externí odkaz:
http://arxiv.org/abs/2410.12591
Autor:
Grzyb, Mateusz, Krzyziński, Mateusz, Sobieski, Bartłomiej, Spytek, Mikołaj, Pieliński, Bartosz, Dan, Daniel, Wróblewska, Anna
This project explores the application of Natural Language Processing (NLP) techniques to analyse United Nations General Assembly (UNGA) speeches. Using NLP allows for the efficient processing and analysis of large volumes of textual data, enabling th
Externí odkaz:
http://arxiv.org/abs/2406.13553
Despite increasing progress in development of methods for generating visual counterfactual explanations, especially with the recent rise of Denoising Diffusion Probabilistic Models, previous works consider them as an entirely local technique. In this
Externí odkaz:
http://arxiv.org/abs/2404.12488
Autor:
Jankowski, Krzysztof, Sobieski, Bartlomiej, Kwiatkowski, Mateusz, Szulc, Jakub, Janik, Michal, Baniecki, Hubert, Biecek, Przemyslaw
Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications. The Segment Anything Model is one of the first and most well-known foundation mod
Externí odkaz:
http://arxiv.org/abs/2404.02067
Autor:
Bombiński, Przemysław, Szatkowski, Patryk, Sobieski, Bartłomiej, Kwieciński, Tymoteusz, Płotka, Szymon, Adamek, Mariusz, Banasiuk, Marcin, Furmanek, Mariusz I., Biecek, Przemysław
Lung mask creation lacks well-defined criteria and standardized guidelines, leading to a high degree of subjectivity between annotators. In this study, we assess the underestimation of lung regions on chest X-ray segmentation masks created according
Externí odkaz:
http://arxiv.org/abs/2402.11510
Autor:
Baniecki, Hubert, Sobieski, Bartlomiej, Szatkowski, Patryk, Bombinski, Przemyslaw, Biecek, Przemyslaw
Publikováno v:
Artificial Intelligence in Medicine, vol. 159, 103026, 2025
Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, only a few interpretable machine learning methods comply with its challen
Externí odkaz:
http://arxiv.org/abs/2303.09817
Autor:
Baniecki, Hubert, Sobieski, Bartlomiej, Szatkowski, Patryk, Bombinski, Przemyslaw, Biecek, Przemyslaw
Publikováno v:
In Artificial Intelligence In Medicine January 2025 159
Autor:
Baniecki, Hubert, Sobieski, Bartlomiej, Bombiński, Przemysław, Szatkowski, Patryk, Biecek, Przemysław
To what extent can the patient's length of stay in a hospital be predicted using only an X-ray image? We answer this question by comparing the performance of machine learning survival models on a novel multi-modal dataset created from 1235 images wit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3191bbb4126a8b8a5b4965a19ee602e