Chan Vese Attention U-Net - An Attention Mechanism for Robust Segmentation
Offered By: Conference GSI via YouTube
Course Description
Overview
Explore an innovative attention mechanism for robust segmentation in this 20-minute conference talk from GSI. Learn about the Chan Vese Attention U-Net, a novel approach that enhances segmentation accuracy and reliability across various applications. Discover how this advanced technique combines the strengths of the Chan-Vese model with attention mechanisms to improve U-Net performance. Gain insights into the architecture, implementation, and potential benefits of this cutting-edge segmentation method for computer vision and image processing tasks.
Syllabus
Chan Vese Attention U-Net: An attention mechanism for robust segmentation
Taught by
Conference GSI
Related Courses
PyTorch and Monai for AI Healthcare Imaging - Python Machine Learning CoursefreeCodeCamp PyTorch Image Segmentation Tutorial with U-NET - Everything From Scratch
Aladdin Persson via YouTube The Role of Data and Models for Deep-Learning Based Image Reconstruction
Institute for Pure & Applied Mathematics (IPAM) via YouTube Creating an Animal Segmentation Model with U-Net and TensorFlow Keras
Eran Feit via YouTube Medical Melanoma Detection - TensorFlow U-Net Tutorial
Eran Feit via YouTube