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1. 发明申请

WO2018125580A1 GLAND SEGMENTATION WITH DEEPLY-SUPERVISED MULTI-LEVEL DECONVOLUTION NETWORKS 审中-公开
公开(公告)号：WO2018125580A1
公开(公告)日：2018-07-05
申请号：PCT/US2017/066227
申请日：2017-12-13
申请人： KONICA MINOLTA LABORATORY U.S.A., INC.
发明人： ZHU, Jingwen , ZHANG, Yongmian
IPC分类号： G06N3/02 , G06N3/04 , G06N3/06 , G06N3/08 , A61K35/22
摘要： Pathological analysis needs instance-level labeling on a histologic image with high accurate boundaries required. To this end, embodiments of the present invention provide a deep model that employs the DeepLab basis and the multi-layer deconvolution network basis in a unified model. The model is a deeply supervised network that allows to represent multi-scale and multi-level features. It achieved segmentation on the benchmark dataset at a level of accuracy which is significantly beyond all top ranking methods in the 2015 MICCAI Gland Segmentation Challenge. Moreover, the overall performance of the model surpasses the state-of-the-art Deep Multi-channel Neural Networks published most recently, and the model is structurally much simpler, more computational efficient and weight-lighted to learn.

2. 发明申请

WO2018052587A1 METHOD AND SYSTEM FOR CELL IMAGE SEGMENTATION USING MULTI-STAGE CONVOLUTIONAL NEURAL NETWORKS 审中-公开
标题翻译：使用多级卷积神经网络进行细胞图像分割的方法和系统
公开(公告)号：WO2018052587A1
公开(公告)日：2018-03-22
申请号：PCT/US2017/046173
申请日：2017-08-09
申请人： KONICA MINOLTA LABORATORY U.S.A., INC.
发明人： ZHANG, Yongmian , ZHU, Jingwen
IPC分类号： G06N3/02
CPC分类号： G06K9/6262 , G06K9/0014 , G06K9/34 , G06K9/6256 , G06N3/0454 , G06N3/08
摘要： An artificial neural network system for image classification, including multiple independent individual convolutional neural networks (CNNs) connected in multiple stages, each CNN configured to process an input image to calculate a pixelwise classification. The output of an earlier stage CNN, which is a class score image having identical height and width as its input image and a depth of N representing the probabilities of each pixel of the input image belonging to each of N classes, is input into the next stage CNN as input image. When training the network system, the first stage CNN is trained using first training images and corresponding label data; then second training images are forward propagated by the trained first stage CNN to generate corresponding class score images, which are used along with label data corresponding to the second training images to train the second stage CNN.
摘要翻译：一种用于图像分类的人工神经网络系统，其包括多级连接的多个独立单独卷积神经网络（CNN），每个CNN被配置为处理输入图像以计算像素级分类。作为具有与其输入图像相同的高度和宽度的类别评分图像以及表示属于N个类别中的每一个的输入图像的每个像素的概率的深度N的前级CNN的输出被输入到下一个阶段CNN作为输入图像。在训练网络系统时，使用第一训练图像和相应的标签数据训练第一级CNN; 则第二训练图像由训练的第一级CNN向前传播以生成对应的类别评分图像，其与对应于第二训练图像的标签数据一起使用以训练第二级CNN。

3. 发明申请

WO2016033279A1 METHOD AND SYSTEM OF TEMPORAL SEGMENTATION FOR GESTURE ANALYSIS 审中-公开
标题翻译：方法分析的时间分割方法与系统
公开(公告)号：WO2016033279A1
公开(公告)日：2016-03-03
申请号：PCT/US2015/047095
申请日：2015-08-27
申请人： KONICA MINOLTA LABORATORY U.S.A., INC.
发明人： AUGE, Quentin , ZHANG, Yongmian , GU, Haisong
IPC分类号： G06F3/01
CPC分类号： G06K9/00342 , G06K9/00201 , G06K9/00765
摘要： A method, system and non-transitory computer readable medium are disclosed for recognizing gestures, the method includes capturing at least one three-dimensional (3D) video stream of data on a subject; extracting a time-series of skeletal data from the at least one 3D video stream of data; isolating a plurality of points of abrupt content change called temporal cuts, the plurality of temporal cuts defining a set of non-overlapping adjacent segments partitioning the time-series of skeletal data; identifying among the plurality of temporal cuts, temporal cuts of the time-series of skeletal data having a positive acceleration; and classifying each of the one or more pair of consecutive cuts with the positive acceleration as a gesture boundary.
摘要翻译：公开了一种用于识别手势的方法，系统和非暂时性计算机可读介质，所述方法包括：在对象上捕获数据的至少一个三维（3D）视频流; 从所述至少一个3D视频数据流中提取骨架数据的时间序列; 分离称为时间切割的多个突变内容变化点，所述多个时间切割定义了划分所述时间序列骨骼数据的一组不重叠的相邻分段; 在多个时间切割之间识别具有正加速度的骨架数据的时间序列的时间切割; 并且将正加速度的一个或多个连续切片中的每一个分类为手势边界。

4. 发明申请

WO2018052586A1 METHOD AND SYSTEM FOR MULTI-SCALE CELL IMAGE SEGMENTATION USING MULTIPLE PARALLEL CONVOLUTIONAL NEURAL NETWORKS 审中-公开
标题翻译：利用多个并行卷积神经网络进行多尺度细胞图像分割的方法和系统
公开(公告)号：WO2018052586A1
公开(公告)日：2018-03-22
申请号：PCT/US2017/046151
申请日：2017-08-09
申请人： KONICA MINOLTA LABORATORY U.S.A., INC.
发明人： ZHU, Jingwen , ZHANG, Yongmian
IPC分类号： G06N3/02
CPC分类号： G06K9/6256 , G06K9/4628 , G06K9/6268 , G06K9/6274 , G06K9/6292 , G06N3/0454 , G06T3/4046
摘要： An artificial neural network system for image classification, formed of multiple independent individual convolutional neural networks (CNNs), each CNN being configured to process an input image patch to calculate a classification for the center pixel of the patch. The multiple CNNs have different receptive field of views for processing image patches of different sizes centered at the same pixel. A final classification for the center pixel is calculated by combining the classification results from the multiple CNNs. An image patch generator is provided to generate the multiple input image patches of different sizes by cropping them from the original input image. The multiple CNNs have similar configurations, and when training the artificial neural network system, one CNN is trained first, and the learned parameters are transferred to another CNN as initial parameters and the other CNN is further trained. The classification includes three classes, namely background, foreground, and edge.
摘要翻译：用于图像分类的人工神经网络系统由多个独立的单独卷积神经网络（CNN）形成，每个CNN被配置为处理输入图像块以计算该块的中心像素的分类。多个CNN具有不同的接受视野，用于处理以相同像素为中心的不同大小的图像块。通过组合来自多个CNN的分类结果来计算中心像素的最终分类。提供图像补丁生成器以通过从原始输入图像中裁剪出不同大小的多个输入图像补丁。多个CNN具有相似的配置，并且在训练人工神经网络系统时，首先训练一个CNN，并且将所学习的参数作为初始参数转移到另一个CNN，并且另一个CNN被进一步训练。分类包括三个类别，即背景，前景和边缘。

5. 发明申请

WO2018005413A1 METHOD AND SYSTEM FOR CELL ANNOTATION WITH ADAPTIVE INCREMENTAL LEARNING 审中-公开
标题翻译：具有自适应增量学习的细胞注释方法和系统
公开(公告)号：WO2018005413A1
公开(公告)日：2018-01-04
申请号：PCT/US2017/039378
申请日：2017-06-27
申请人： KONICA MINOLTA LABORATORY U.S.A., INC.
发明人： ZHANG, Yongmian , ZHU, Jingwen
IPC分类号： C08F116/06 , C12N5/07 , G06K9/38 , G06N99/00
摘要： A method, a computer readable medium, and a system for cell annotation are disclosed. The method includes receiving at least one new cell image for cell detection; extracting cell features from the at least one new cell image; comparing the extracted cell features to a matrix of cell features of each class to predict a closest class, wherein the matrix of cell features has been generated from at least initial training data comprising at least one cell image; detecting cell pixels from the extracted cell features of the at least one new cell image using the predicted closest class to generate a likelihood map; extracting individual cells from the at least one cell image by segmenting the individual cells from the likelihood map; performing a machine annotation on the extracted individual cells from the at least one new cell image to identify cells, non-cell pixels, and/or cell boundaries; calculating a confidence level for the machine annotation on the extracted individual cells from the at least one new cell image; and modifying the machine annotation if the confidence level is below a predetermined threshold.
摘要翻译：公开了用于单元注释的方法，计算机可读介质和系统。该方法包括接收用于小区检测的至少一个新的小区图像; 从所述至少一个新细胞图像提取细胞特征; 将提取的细胞特征与每个类的细胞特征的矩阵进行比较以预测最接近的类，其中细胞特征的矩阵已经从至少包括至少一个细胞图像的初始训练数据生成; 使用预测的最近类别从所提取的至少一个新细胞图像的细胞特征中检测细胞像素以生成似然图; 通过从所述似然图中分割所述单个细胞从所述至少一个细胞图像中提取单个细胞; 对来自至少一个新细胞图像的提取的个体细胞执行机器注释以识别细胞，非细胞像素和/或细胞边界; 从所述至少一个新细胞图像计算所提取的个体细胞上的机器注释的置信度; 并且如果置信水平低于预定阈值则修改机器注释。

6. 发明公开

EP3478728A1 METHOD AND SYSTEM FOR CELL ANNOTATION WITH ADAPTIVE INCREMENTAL LEARNING 有权
公开(公告)号：EP3478728A1
公开(公告)日：2019-05-08
申请号：EP17821044.9
申请日：2017-06-27
申请人： Konica Minolta Laboratory U.S.A., Inc.
发明人： ZHANG, Yongmian , ZHU, Jingwen
IPC分类号： C08F116/06 , C12N5/07 , G06K9/38 , G06N99/00

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