1 code implementation • 11 Sep 2024 • Michal K. Grzeszczyk, Przemysław Korzeniowski, Samer Alabed, Andrew J. Swift, Tomasz Trzciński, Arkadiusz Sitek
We test TabMixer for mPAP estimation and show that it enhances the performance of Convolutional Neural Networks, 3D-MLP and Vision Transformers while being competitive with previous modules for imaging and tabular data.
1 code implementation • 12 Jul 2024 • Tomasz Szczepański, Michal K. Grzeszczyk, Szymon Płotka, Arleta Adamowicz, Piotr Fudalej, Przemysław Korzeniowski, Tomasz Trzciński, Arkadiusz Sitek
When labels are not available during inference, our model infers the necessary conditioning embedding directly from the input data, thanks to a feed-forward network learned during the training phase.
no code implementations • 8 Feb 2024 • Kelly Payette, Céline Steger, Roxane Licandro, Priscille de Dumast, Hongwei Bran Li, Matthew Barkovich, Liu Li, Maik Dannecker, Chen Chen, Cheng Ouyang, Niccolò McConnell, Alina Miron, Yongmin Li, Alena Uus, Irina Grigorescu, Paula Ramirez Gilliland, Md Mahfuzur Rahman Siddiquee, Daguang Xu, Andriy Myronenko, Haoyu Wang, Ziyan Huang, Jin Ye, Mireia Alenyà, Valentin Comte, Oscar Camara, Jean-Baptiste Masson, Astrid Nilsson, Charlotte Godard, Moona Mazher, Abdul Qayyum, Yibo Gao, Hangqi Zhou, Shangqi Gao, Jia Fu, Guiming Dong, Guotai Wang, ZunHyan Rieu, HyeonSik Yang, Minwoo Lee, Szymon Płotka, Michal K. Grzeszczyk, Arkadiusz Sitek, Luisa Vargas Daza, Santiago Usma, Pablo Arbelaez, Wenying Lu, WenHao Zhang, Jing Liang, Romain Valabregue, Anand A. Joshi, Krishna N. Nayak, Richard M. Leahy, Luca Wilhelmi, Aline Dändliker, Hui Ji, Antonio G. Gennari, Anton Jakovčić, Melita Klaić, Ana Adžić, Pavel Marković, Gracia Grabarić, Gregor Kasprian, Gregor Dovjak, Milan Rados, Lana Vasung, Meritxell Bach Cuadra, Andras Jakab
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
1 code implementation • 10 Jan 2024 • Michal K. Grzeszczyk, Tomasz Trzciński, Arkadiusz Sitek
In this work, we present a novel, machine-learning approach for constructing Multiclass Interpretable Scoring Systems (MISS) - a fully data-driven methodology for generating single, sparse, and user-friendly scoring systems for multiclass classification problems.
no code implementations • 21 Dec 2023 • Michal K. Grzeszczyk, Tadeusz Satlawa, Angela Lungu, Andrew Swift, Andrew Narracott, Rod Hose, Tomasz Trzcinski, Arkadiusz Sitek
We show using the ablation study, that physics-informed feature engineering based on models of blood circulation increases the performance of Gradient Boosting Decision Trees-based algorithms for classification of PH and regression of values of mPAP.
no code implementations • 21 Dec 2023 • Michal K. Grzeszczyk, Anna Lisowska, Arkadiusz Sitek, Aneta Lisowska
Automatic detection and tracking of emotional states has the potential for helping individuals with various mental health conditions.
1 code implementation • 27 Oct 2023 • Michal K. Grzeszczyk, Szymon Płotka, Beata Rebizant, Katarzyna Kosińska-Kaczyńska, Michał Lipa, Robert Brawura-Biskupski-Samaha, Przemysław Korzeniowski, Tomasz Trzciński, Arkadiusz Sitek
In this paper, we introduce TabAttention, a novel module that enhances the performance of Convolutional Neural Networks (CNNs) with an attention mechanism that is trained conditionally on tabular data.
no code implementations • 7 Feb 2023 • Michal K. Grzeszczyk, Paulina Adamczyk, Sylwia Marek, Ryszard Pręcikowski, Maciej Kuś, M. Patrycja Lelujko, Rosmary Blanco, Tomasz Trzciński, Arkadiusz Sitek, Maciej Malawski, Aneta Lisowska
The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement.
no code implementations • 6 Sep 2022 • Michal K. Grzeszczyk, Szymon Płotka, Arkadiusz Sitek
Cardiac Magnetic Resonance Imaging is commonly used for the assessment of the cardiac anatomy and function.
1 code implementation • 19 May 2022 • Szymon Płotka, Michal K. Grzeszczyk, Robert Brawura-Biskupski-Samaha, Paweł Gutaj, Michał Lipa, Tomasz Trzciński, Arkadiusz Sitek
Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery.