- Investigation of Energy-Efficient AI Model Architectures and Compression Techniques for “Green” Fetal Brain Segmentation
- Automated glioma multiclass tumor classification
- Style transfer between microscopy and magnetic resonance imaging via generative adversarial network in small sample size settings
- Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features
Research / Computational Neuroscience
Monika Pytlarz
PhD Student in Computational Neuroscience
Monika is a PhD student working on AI analysis of tumors, with a focus on gliomas. Her research combines radiology, histopathology, and genomics to improve tumor classification and patient risk stratification, despite the biological heterogeneity of gliomas. The project’s computational image analysis of immunostaining provides insights into the tumor immune microenvironment, supporting the development of more precise therapeutic strategies. She also investigates cross-modal image translation, including the synthesis of histology from MRI scans.
Monika obtained a BSc in Electroradiology from Collegium Medicum of the Jagiellonian University and an MSc in Bioinformatics from JU, and brings clinical experience from hospital diagnostic imaging departments. She explores translating AI models into clinical practice via computer-aided diagnosis tools and PACS integration.