AWDS-网络：用于描述不同乳腺肿块特征的全场自动分割网络

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-01-08 DOI:10.1080/09540091.2023.2289836

Jiajia Jiao, Yingzhao Chen, Zhiyu Li, Tien-Hsiung Weng

引用次数: 0

摘要

乳房肿块的大小、形状和位置各不相同，这使得在统一的深度学习网络中进行准确的图像分割更具挑战性。因此，基于 U-net 网络，一种自适应的自动乳房肿块图像分割技术可用于乳房肿块的图像分割。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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AWDS-net: automatic whole-field segmentation network for characterising diverse breast masses

Diverse breast masses in size, shape and place make accurate image segmentation more challenging in a unified deep-learning network. Therefore, based on the U-net network, an adaptive automatic who...

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.