This repository contains a deep learning-based medical image segmentation system developed at the Budapest University of Technology and Economics. The system utilizes an encoder-decoder architecture, ...

(* U-Net architecture is a deep learning image segmentation architecture introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. Its U shape design consists of two parts. The left ...

Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, ...

In this proposed study, SegNet is used for segmentation of cellular structures in high-resolution histopathological images. SegNet is a deep convolutional encoder-decoder architecture used for ...

Keywords: deep learning, U-net, medical image segmentation, pulmonarty embolism, CNN -convolutional neural network. Citation: Zhan S, Yuan Q, Lei X, Huang R, Guo L, Liu K and Chen R (2024) BFNet: a ...

The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.