- SimuScope: Realistic Endoscopic Synthetic Dataset Generation through Surgical Simulation and Diffusion Models
- BabyNet++: Fetal Birth Weight Prediction using Biometry Multimodal Data Acquired Less Than 24 Hours Before Delivery
- Why is the winner the best?
- Minimal data requirement for realistic endoscopic image generation with Stable Diffusion
- TabAttention: Learning Attention Conditionally on Tabular Data
- Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation
- Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns
- BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video
- POTHER: Patch-Voted Deep Learning-based Chest X-ray Bias Analysis for COVID-19 Detection
- CXR-FL: Deep Learning-based Chest X-ray Image Analysis Using Federated Learning
- FetalNet: Multi-Task Deep Learning Framework for Fetal Ultrasound Biometric Measurements
- Deep learning fetal ultrasound video model match human observers in biometric measurements
- Virtual Reality Simulator for Fetoscopic Spina Bifida Repair Surgery
Research / Medical Imaging and Robotics
Szymon Płotka
PostDoc in Medical Imaging and Robotics
Szymon Płotka obtained his PhD in Computer Science in 2024 from the Informatics Institute at the University of Amsterdam, where his research focused on applying deep learning to enhance prenatal care. His doctoral thesis, “Enhancing Prenatal Care Through Deep Learning,” explored advanced machine learning algorithms for medical image analysis, with a particular emphasis on applications in fetal video ultrasound imaging.
Szymon’s research interests lie at the intersection of computer vision, machine learning, and deep learning-based algorithms for medical image analysis. He is particularly interested in developing innovative AI-driven solutions for improving diagnostic accuracy, integrating multimodal data sources, and optimizing healthcare workflows. His work aims to bridge the gap between cutting-edge artificial intelligence and real-world clinical applications, contributing to more efficient and accessible medical imaging technologies.