Abstract: Deep neural network (DNN)-based image segmentation has the endemic problems caused by iterative sampling, i.e., inaccurate and uncertain boundaries. On the other hand, conventional image ...

Neural networks are powerful tools for processing visual inputs ... function of recurrent connections in biological visual systems. We study image segmentation using spatiotemporal dynamics in a ...

Image segmentation finds application in various areas of image processing and computer vision ... This paper proposes convolutional neural network (CNN) based image segmentation for image annotation ...

This paper proposes an end-to-end trained fully convolutional neural network model to process 3D image ... using the Dice coefficient is proposed to be optimized, and the authors demonstrate the fast ...

And for image processing ... the usual way through forward inference and backpropagation. At the end is a pixel-wise prediction layer that will create the segmentation map. The decoder has a number of ...

Therefore, this paper completes the task of brain tumor segmentation by building a self-supervised deep learning network. Specifically, it designs a multi-modal encoder-decoder network based on the ..

This study focuses on the specific application of YOLOv5 in brain tumor image ... advanced network structures such as the CSPDarknet backbone and anchor-based detection and segmentation mechanisms, ...