- 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 / Health Informatics Group at Sano (HIGS)
Szymon Płotka
PhD Student in Health Informatics
He obtained M.Sc. in Medical Informatics in 2019 from Warsaw University of Technology. Currently pursuing his PhD in the medical image analysis at Sano Centre and Warsaw University of Technology. His main research interests are computer vision, machine learning and deep learning-based fetal ultrasound imaging and image-guided therapy.