专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

31. 发明申请

US20180144214A1 DEEP LEARNING MEDICAL SYSTEMS AND METHODS FOR IMAGE RECONSTRUCTION AND QUALITY EVALUATION 审中-公开
公开(公告)号：US20180144214A1
公开(公告)日：2018-05-24
申请号：US15360742
申请日：2016-11-23
申请人： General Electric Company
发明人： Jiang Hsieh , Gopal Avinash , Saad Sirohey , Xin Wang , Zhye Yin , Bruno De Man
IPC分类号： G06K9/62 , G06N3/04 , G06N3/08 , G06T7/00 , G06K9/46
CPC分类号： G06K9/6265 , G06K9/4604 , G06N3/0454 , G06N3/084 , G06T7/0012 , G06T2207/10081 , G06T2207/10088 , G06T2207/10104 , G06T2207/10108 , G06T2207/10116 , G06T2207/10132 , G06T2207/30168
摘要： Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

32. 发明申请

US20180137388A1 METHOD AND APPARATUS FOR ANALYZING FACIAL IMAGE 审中-公开
公开(公告)号：US20180137388A1
公开(公告)日：2018-05-17
申请号：US15795677
申请日：2017-10-27
申请人： Samsung Electronics Co., Ltd.
发明人： Youngsung KIM , Byungin YOO , Deheng QIAN , Hui ZHANG , Chang Kyu CHOI , He ZHENG , Jae-Joon HAN , Jingtao XU , Tianchu GUO
IPC分类号： G06K9/62 , G06K9/00 , G06K9/66
CPC分类号： G06K9/623 , G06K9/00281 , G06K9/00288 , G06K9/6265 , G06K9/627 , G06K9/6297 , G06K9/66 , G06N3/0454
摘要： A method to analyze a facial image includes: inputting a facial image to a residual network including residual blocks that are sequentially combined and arranged in a direction from an input to an output; processing the facial image using the residual network; and acquiring an analysis map from an output of an N-th residual block among the residual blocks using a residual deconvolution network, wherein the residual network transfers the output of the N-th residual block to the residual deconvolution network, and N is a natural number that is less than a number of all of the residual blocks, and wherein the residual deconvolution network includes residual deconvolution blocks that are sequentially combined, and the residual deconvolution blocks correspond to respective residual blocks from a first residual block among the residual blocks to the N-th residual block.

33. 发明申请

US20180121765A1 INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND STORAGE MEDIUM 审中-公开
公开(公告)号：US20180121765A1
公开(公告)日：2018-05-03
申请号：US15785187
申请日：2017-10-16
申请人： CANON KABUSHIKI KAISHA
发明人： Masafumi Takimoto
IPC分类号： G06K9/62
CPC分类号： G06K9/6276 , G06K9/6228 , G06K9/6231 , G06K9/6234 , G06K9/6248 , G06K9/6254 , G06K9/6265
摘要： Before dimension reduction is performed while local data distribution is stored as neighborhood data, a distance between data to be subjected to the dimension reduction is calculated, and a parameter (a neighborhood number of the k-nearest neighbor algorithm or a size of a hypersphere) which determines the neighborhood data is determined for each data to be subjected to the dimension reduction. Thereafter, the dimension reduction is performed on the target data based on the determined parameter.

34. 发明申请

US20180121761A1 COLD START MACHINE LEARNING ALGORITHM 审中-公开
公开(公告)号：US20180121761A1
公开(公告)日：2018-05-03
申请号：US15858224
申请日：2017-12-29
申请人： Microsoft Technology Licensing, LLC
发明人： Uri Merhav , Dan Shacham
IPC分类号： G06K9/62
CPC分类号： G06K9/6265 , G06K9/6267 , G06K9/6277 , G06N3/0454
摘要： In an example embodiment, a first plurality of images stored on a computing device is identified, each image having an indication that it depicts a first member of a social networking service. The first plurality of images is used as training data to a first machine learning algorithm to train a first machine learning algorithm model corresponding to the first member, the first machine learning algorithm model corresponding to the first member designed to calculate a member likelihood score for a candidate image. Then a second plurality of images stored on the computing device is obtained. Each image of the second plurality of images is fed to the first machine learning algorithm model corresponding to the first member, obtaining a member likelihood score for each of the second plurality of images. Then, based on the member likelihood scores for the second plurality of images, one or more member images are selected.

35. 发明申请

US20180032841A1 Medical Scanner Teaches Itself To Optimize Clinical Protocols And Image Acquisition 审中-公开
公开(公告)号：US20180032841A1
公开(公告)日：2018-02-01
申请号：US15224710
申请日：2016-08-01
申请人： Siemens Healthcare GmbH
发明人： Stefan Kluckner , Dorin Comaniciu
IPC分类号： G06K9/62
CPC分类号： G06K9/6263 , G06F19/00 , G06K9/00 , G06K9/6265 , G06N3/006 , G06N3/0454 , G06N99/005 , G16H40/63
摘要： A computer-implemented method for identifying an optimal set of parameters for medical image acquisition includes receiving a set of input parameters corresponding to a medical imaging scan of a patient and using a model of operator parameter selection to determine a set of optimal target parameter values for a medical image scanner based on the set of input parameters. The medical imaging scan of the patient is performed using the set of optimal target parameter values to acquire one or more images and feedback is collected from one or more users in response to acquisition of the one or more images. This feedback is used to update the model of operator parameter selection, thereby yielding an updated model of operator parameter selection.

36. 发明授权

US09842280B2 System and method for evaluating a classifier implemented within an image signal processor 有权
公开(公告)号：US09842280B2
公开(公告)日：2017-12-12
申请号：US14932926
申请日：2015-11-04
申请人： OmniVision Technologies, Inc.
发明人： Ming Kai Hsu
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06K9/6265 , G06K9/00228 , G06K9/6231 , G06K9/6253 , G06K9/6267
摘要： A system for evaluating a classifier of an image signal processor (ISP) includes (i) a microprocessor and (ii) memory storing training images, the microprocessor being capable of sending each training image to the ISP. The system includes machine-readable instructions stored within the memory and executed by the microprocessor capable of: (i) selecting a subset of images based upon a divider position, (ii) controlling the ISP to classify each image as belonging or not belonging to an object class, (iii) determining a positive-match count, (iv) determining an error count based upon the positive-match count and total number of training images belonging to the object class, (v) repeating, for other divider positions, steps of selecting, controlling, and determining to identify an optimal divider position and a minimum-error count; and (vi) determining the classifier's optimality by comparing the optimal divider position to a predetermined optimal divider position and a predetermined minimum-error count.

37. 发明申请

US20170270666A1 COMPUTATIONAL PATHOLOGY SYSTEMS AND METHODS FOR EARLY-STAGE CANCER PROGNOSIS 审中-公开
公开(公告)号：US20170270666A1
公开(公告)日：2017-09-21
申请号：US15613113
申请日：2017-06-02
申请人： Ventana Medical Systems, Inc.
发明人： Michael Barnes , Srinivas Chukka , Bonnie LaFleur , Chang Xu
IPC分类号： G06T7/00 , G06K9/62 , G06F19/00 , G01N33/574
CPC分类号： G06T7/0012 , G01N33/57415 , G01N2800/52 , G01N2800/54 , G06K9/00147 , G06K9/6265 , G06T7/11 , G06T7/12 , G06T2207/10056 , G06T2207/30024 , G16H50/30
摘要： The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage cancer patients by predicting a recurrence risk of the patient and to categorize the patient into a high or low risk group. A series of slides depicting serial sections of cancerous tissue are automatically analyzed by a digital pathology system, a score for the sections is calculated, and a Cox proportional hazards regression model is used to stratify the patient into a low or high risk group. The Cox proportional hazards regression model may be used to determine a whole-slide scoring algorithm based on training data comprising survival data for a plurality of patients and their respective tissue sections. The coefficients may differ based on different types of image analysis operations applied to either whole-tumor regions or specified regions within a slide.

38. 发明申请

US20170049980A1 INTUBATION SYSTEMS AND METHODS BASED ON AIRWAY PATTERN IDENTIFICATION 审中-公开
标题翻译：基于航空图案识别的线路系统和方法
公开(公告)号：US20170049980A1
公开(公告)日：2017-02-23
申请号：US15346656
申请日：2016-11-08
申请人： Chunyuan Qiu
发明人： Chunyuan Qiu
IPC分类号： A61M16/04 , G06K9/46 , G06K9/62 , A61B1/267 , G06F17/30
CPC分类号： A61M16/0411 , A61B1/00009 , A61B1/00135 , A61B1/0051 , A61B1/015 , A61B1/267 , A61M16/0003 , A61M16/0418 , A61M16/0488 , A61M16/0493 , A61M16/0495 , A61M2016/0015 , A61M2016/0027 , A61M2016/003 , A61M2016/0413 , A61M2205/3327 , A61M2205/3334 , A61M2205/50 , A61M2210/1032 , A61M2230/40 , G06F17/30598 , G06K9/46 , G06K9/6265 , G06K9/6277 , G06K2209/05
摘要： An intubation system of the present disclosure intubates based on an airway pattern indicating a trachea opening. The airway pattern is determined from analysis of airway data detected by a trachea identifying device disposed on a moveable guide stylet of the intubation system. A navigation element is generated based on the airway pattern. In one embodiment, the airway pattern is a gas exchange pattern indicating a trachea opening. In another embodiment, the trachea opening transition pattern is a topographic pattern indicating a trachea opening. The guide stylet is capable of moving in a plurality of degrees of freedom in the airway following the guidance from the navigation element.
摘要翻译：本发明的插管系统基于指示气管开口的气道图案来插管。气道图案是由设置在插管系统的可移动导管管路上的气管识别装置检测到的气道数据的分析确定的。基于气道模式生成导航元件。在一个实施例中，气道图案是指示气管开口的气体交换模式。在另一个实施例中，气管开放过渡模式是指示气管开口的地形图案。引导探针能够沿着导航元件的引导在气道中以多个自由度移动。

39. 发明申请

US20170032247A1 MEDIA CLASSIFICATION 审中-公开
标题翻译：媒体分类
公开(公告)号：US20170032247A1
公开(公告)日：2017-02-02
申请号：US14859082
申请日：2015-09-18
申请人： QUALCOMM Incorporated
发明人： Henok Tefera TADESSE , Avijit CHAKRABORTY , David Jonathan JULIAN , Henricus Meinardus STOKMAN , Ork DE ROOIJ , Koen Erik Adriaan VAN DE SANDE , Venkata Sreekanta Reddy ANNAPUREDDY
IPC分类号： G06N3/08 , G06N3/04 , G06N99/00
CPC分类号： G06N3/088 , G06K9/6265 , G06N3/02 , G06N3/04 , G06N20/00
摘要： Multi-label classification is improved by determining thresholds and/or scale factors. Selecting thresholds for multi-label classification includes sorting a set of label scores associated with a first label to create an ordered list. Precision and recall values are calculated corresponding to a set of candidate thresholds from score values. The threshold is selected from the candidate thresholds for the first label based on target precision values or recall values. A scale factor is also selected for an activation function for multi-label classification where a metric of scores within a range is calculated. The scale factor is adjusted when the metric of scores are not within the range.
摘要翻译：通过确定阈值和/或比例因子来提高多标签分类。选择多标签分类的阈值包括对与第一标签相关联的一组标签分数进行排序以创建有序列表。根据得分值对应于一组候选阈值计算精确度和回忆值。基于目标精度值或召回值，从第一标签的候选阈值中选择阈值。还为多标签分类的激活功能选择比例因子，其中计算范围内的分数的度量。当分数的度量不在该范围内时，调整比例因子。

40. 发明申请

US20160205382A1 METHOD AND APPARATUS FOR GENERATING A LABELED IMAGE BASED ON A THREE DIMENSIONAL PROJECTION 审中-公开
标题翻译：用于生成基于三维投影的标记图像的方法和装置
公开(公告)号：US20160205382A1
公开(公告)日：2016-07-14
申请号：US14592280
申请日：2015-01-08
申请人： Nokia Technologies Oy
发明人： Xin Chen , Huang Xinyu
IPC分类号： H04N13/02 , G06T3/00 , G06K9/62 , G06K9/52 , G06K9/46
CPC分类号： G06K9/00208 , G06K9/6265
摘要： A method, apparatus and computer program product are provided for generating a labeled image based on a three dimensional (3D) projection. A method is provided including receiving an input image and a 3D shape model associated with an object, generating a 3D projection based on the input image and the 3D shape model, extracting object features associated with a landmark location from the input image, estimating an object position based on the extracted features, determining a distance between a 3D shape landmark location and a true landmark location, applying a regression model based on the extracted feature and the distance between the 3D shape landmark location and the true landmark location, updating the 3D shape model landmark location of the 3D projection based on the regression, and generating a labeled image based on the updated 3D projection.
摘要翻译：提供了一种基于三维（3D）投影来生成标记图像的方法，装置和计算机程序产品。提供了一种方法，包括接收与对象相关联的输入图像和3D形状模型，基于输入图像和3D形状模型生成3D投影，从输入图像提取与地标位置相关联的对象特征，估计对象基于所提取的特征，确定3D形状地标位置和真实地标位置之间的距离，基于提取的特征和3D形状地标位置与真实地标位置之间的距离应用回归模型，更新3D形状基于回归来模拟3D投影的地标位置，以及基于更新的3D投影生成标记图像。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式