公开(公告)号：US10282663B2

公开(公告)日：2019-05-07

申请号：US15237575

申请日：2016-08-15

Applicant: salesforce.com, inc.

Inventor： Richard Socher ,  Caiming Xiong ,  Kai Sheng Tai

IPC: G06N3/08 ,  G06N3/04 ,  G06F17/50 ,  G06K9/46 ,  G06T7/00 ,  G06K9/00 ,  G06K9/62

Abstract: The technology disclosed uses a 3D deep convolutional neural network architecture (DCNNA) equipped with so-called subnetwork modules which perform dimensionality reduction operations on 3D radiological volume before the 3D radiological volume is subjected to computationally expensive operations. Also, the subnetworks convolve 3D data at multiple scales by subjecting the 3D data to parallel processing by different 3D convolutional layer paths. Such multi-scale operations are computationally cheaper than the traditional CNNs that perform serial convolutions. In addition, performance of the subnetworks is further improved through 3D batch normalization (BN) that normalizes the 3D input fed to the subnetworks, which in turn increases learning rates of the 3D DCNNA. After several layers of 3D convolution and 3D sub-sampling with 3D across a series of subnetwork modules, a feature map with reduced vertical dimensionality is generated from the 3D radiological volume and fed into one or more fully connected layers.

公开(公告)号：US20170046616A1

公开(公告)日：2017-02-16

申请号：US15237575

申请日：2016-08-15

Applicant: salesforce.com, inc.

Inventor： Richard Socher ,  Caiming Xiong ,  Kai Sheng Tai

IPC: G06N3/08 ,  G06F17/50 ,  G06N3/04

CPC classification number: G06N3/088 ,  G06F17/50 ,  G06K9/00201 ,  G06K9/00993 ,  G06K9/4628 ,  G06K9/627 ,  G06K2209/05 ,  G06N3/04 ,  G06N3/0454 ,  G06T7/0012 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06T2207/30041 ,  G06T2207/30068

Abstract: The technology disclosed uses a 3D deep convolutional neural network architecture (DCNNA) equipped with so-called subnetwork modules which perform dimensionality reduction operations on 3D radiological volume before the 3D radiological volume is subjected to computationally expensive operations. Also, the subnetworks convolve 3D data at multiple scales by subjecting the 3D data to parallel processing by different 3D convolutional layer paths. Such multi-scale operations are computationally cheaper than the traditional CNNs that perform serial convolutions. In addition, performance of the subnetworks is further improved through 3D batch normalization (BN) that normalizes the 3D input fed to the subnetworks, which in turn increases learning rates of the 3D DCNNA. After several layers of 3D convolution and 3D sub-sampling with 3D across a series of subnetwork modules, a feature map with reduced vertical dimensionality is generated from the 3D radiological volume and fed into one or more fully connected layers.

Abstract translation: 所公开的技术使用配备有所谓的子网模块的3D深卷积神经网络架构（DCNNA），其在3D放射体积经受计算上昂贵的操作之前对3D放射体积进行降维操作。 此外，子网络通过对3D数据进行不同的3D卷积层路径的并行处理，在多个尺度上卷积3D数据。 这种多尺度操作在计算上比执行串行卷积的传统CNN便宜。 此外，通过对馈送到子网络的3D输入进行归一化的3D批量归一化（BN），进一步提高了子网络的性能，从而提高了3D DCNNA的学习速率。 在通过一系列子网模块进行三维3D卷积和三维子采样与三维子采样之后，从3D放射体积产生具有降低的垂直维数的特征图，并将其馈送到一个或多个完全连接的层。

公开(公告)号：US11416747B2

公开(公告)日：2022-08-16

申请号：US16355290

申请日：2019-03-15

Applicant: salesforce.com, inc.

Inventor： Richard Socher ,  Caiming Xiong ,  Kai Sheng Tai

IPC: G06N3/08 ,  G06N3/04 ,  G06T7/00 ,  G06K9/62 ,  G06F30/00 ,  G06V10/96 ,  G06V10/44 ,  G06V20/64

Abstract: A method of classifying three-dimensional (3D) data includes receiving three-dimensional (3D) data and processing the 3D data using a neural network that includes a plurality of subnetworks arranged in a sequence and the data is processed through each of the subnetworks. Each of the subnetworks is configured to receive an output generated by a preceding subnetwork in the sequence, process the output through a plurality of parallel 3D convolution layer paths of varying convolution volume, process the output through a parallel pooling path, and concatenate output of the 3D convolution layer paths and the pooling path to generate an output representation from each of the subnetworks. Following processing the data through the subnetworks, the method includes processing the output of a last one of the subnetworks in the sequence through a vertical pooling layer to generate an output and classifying the received 3D data based upon the generated output.