Maciej Szymkowski
Maciej Szymkowski is a Ph.D. in the field of Computer Science. From April 2025, he is working with Future Processing as a Senior Machine Learning Engineer. Previously he was with Bialystok University of Technology (Faculty of Computer Science, 2018-2026), Warsaw University of Technology (Faculty of Electronics and Information Technology, 2021-2022) and AGH in Cracow (Faculty of Physics and Applied Computer Science, 2021-2022). He gained his experiences also as a researcher in diversified projects and software developer/engineer in different companies (e.g., SoftServe, Symmetra, Transition Technologies, Hemolens Diagnostics). He was also with Łukasiewicz Research Network - Poznan Institute of Technology (2022-2024) where he was a Head of AI Development section. Maciej Szymkowski is an author or co-author of more than 45 research papers published in JCR journals, as chapters in the books or in international conference proceedings. His main research area is Computer Vision – especially in the field of medicine and transport. He is interested in Vision-Language Models (VLMs), Large Language Models (LLMs) and machine learning algorithms. In his free time, he loves to extend his knowledge, take a long walk, read a book or watch soccer, basketball or hockey. He is a fan of Legia Warsaw, Real Madrid (soccer), New York Knicks (basketball) and Pittsburgh Penguins (hockey).
he/him
Future Processing
AI Researcher and Senior Machine Learning Engineer
@MackSzymkowski
Session
The transition toward Organ-on-Chip (OoC) and Engineered Heart Tissues (EHTs) in pharmacological research necessitates automated tools for longitudinal cell monitoring. Manual tracking of cardiac cycles is labor-intensive and impractical for long-duration studies, highlighting the need for high-throughput segmentation. This study proposes a specialized shallow 2D U-Net architecture utilizing Focal Loss to effectively manage class imbalances between cellular structures and the background in spatial recordings.
The model was trained on a dataset of 1,240 images and validated against 100 test samples, achieving a Dice score of 0.9152 and a mean Intersection over Union (mIoU) of 0.8486 relative to manual ground truths. To contextualize these results, the proposed network was benchmarked against MedSAM, a state-of-the-art foundation model for medical imaging.
While MedSAM offers broad generalization, our lightweight U-Net demonstrates superior computational efficiency and localized precision for the high-frame-rate requirements of cardiac cycle analysis. The findings suggest that a task-specific, loss-optimized architecture provides a high-performance, resource-efficient alternative to large-scale foundation models, facilitating objective and scalable analysis of single-cell behavior in animal-free drug testing environments.