By breaking an image into smaller groups called “image segments,” an image segmentation ... based U-NET technique was used to find extrapolation-based reconstructions for alloys, porous media, and ...

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 ...

Abstract: Image segmentation in medical imaging has long been a problem in radiological image processing ... This article proposes an image segmentation method based on an optimized cellular neural ...

Methods: Segmentation algorithms solely based on image processing involve multiple parameters and ... and then devise algorithms to extract these features. End-to-end deep neural networks take raw ...

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 ...

Abstract: Medical image ... Segmentation of COVID-19 via CT Images" Neural Processing Letters (2022). [paper] Jia, Zijing and Wang, Chaoli and Sun, Zhanquan and Geng, Hongquan and Fu, Hongliang. "A ...

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 ..

MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), they announced today their research on quantum visual computing, exploring the integration of quantum computing with classical ...