生物标记物发现方法 基于深度学习与医学知识图谱约束的多尺度生物标记物发现技术

Introduction

这是一个基于深度学习的多尺度医学影像分割模型，一种自适应尺度卷积神经网络。该模型在端到端训练中引入了3层卷积结构，以自适应地学习图像中每个像素最优的膨胀参数。这种像素级的膨胀参数使得模型能够获得最佳的感受野，从而可以在相应的尺度下提取具有不同大小的物体的信息。

Publications

  1. ASCNET: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning
    Cite this
    Mo Zhang, Jie Zhao, Xiang Li, Li Zhang, Quanzheng Li, 2020/5/22 - 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
    Cited by 2 (Google Schoolar as of June 2, 2021)

Credits

  1. MoZhang zhangmo007@pku.edu.cn
    Contributor

    Center for Data Science, Peking University, China

  2. Jie Zhao jiezhao@pku.edu.cn
    Contributor

    Center for Data Science, Peking University, China

  3. Xiang Li xli60@mgh.harvard.edu
    Contributor

    Radiology, Massachusetts General Hospital, United States of America

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Summary
AccessionBT007198
Tool TypeApplication
CategoryImage analysis
PlatformsLinux/Unix
TechnologiesPython3
User InterfaceTerminal Command Line
Latest Release1.0 (June 2, 2021)
Download Count35
Submitted Bybin dong
Fundings

This work was supported by National Key R&D Program of China (No.2018YFC0910700);