Developed and optimized a 3D U-Net deep learning model for brain tumor segmentation using multimodal MRI scans, focusing on identifying tumor regions through volumetric medical image analysis.
This project focused on automatic brain tumor segmentation from multimodal magnetic resonance imaging (MRI) scans, within the context of the BraTS Challenge.
I designed, trained, and optimized a 3D U-Net model using PyTorch to segment brain tumor regions in three-dimensional medical images.
The project explored deep learning for medical image segmentation, with a focus on learning spatial features from volumetric MRI data and evaluating segmentation performance.
Shared fields: Deep Learning
Developed a multimodal recommendation system for click-through-rate prediction, exploring deep learning architectures that combine multiple data modalities to improve recommendation performance.