1 code implementation • https://www.nature.com 2022 • Zhiming Cui 1, 2, 3, Yu Fang 1, Lanzhuju Mei 1, Bojun Zhang4, 10, BoYu5, Jiameng Liu1, Caiwen Jiang1, Yuhang Sun1, Lei Ma1, Jiawei Huang1, Yang Liu6, Yue Zhao7✉, Chunfeng Lian8✉, Zhongxiang Ding9✉, Min Zhu4✉ & Dinggang Shen1, 3✉ Accurate
In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91. 5% and 93. 0% for tooth and alveolar bone segmentation.
no code implementations • Front Immunol. 2021 • Chenjie Qiu, 1, † Wenxiang Shi, 2, † Huili Wu, 3, † Shenshan Zou, 1 Jianchao Li, 1 Dong Wang, 1 Guangli Liu, 1 Zhenbiao Song, 1 Xintao Xu, 1 Jiandong Hu, corresponding author 1, * and Hui Gengcorresponding author 1, *
We finally used qRT–PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction.
1 code implementation • 2019 IEEE 2019 • Zongxu Pan1, 2*, Xianjie Bao1, Yueting Zhang1, Bowei Wang1, Quanzhi An1, 3, and Bin Lei1, 2
Different from classification networks that predict the category of one sample, the Siamese network implements a metric learning to measure the similarity between two samples.
no code implementations • 地 球 物 理 学 报 2007 • 唐秋华1, 2, 3, 刘保华2, 陈永奇3, 周兴华2, 丁继胜2
学习向量量化(Learning Vector Quantization, LVQ)神经网络在声学底质分类中具有广泛应用. 常用的LVQ 神经网络存在神经元未被充分利用以及算法对初值敏感的问题, 影响底质分类精度. 本文提出采用遗传算法 (Genetic Algorithms, GA)优化神经网络的初始值, 将GA与LVQ神经网络结合起来, 迅速得到最佳的神经网络初始权 值向量, 实现对海底基岩、砾石、砂、细砂以及泥等底质类型的快速、准确识别. 将其应用于青岛胶州湾海区底质分