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公开(公告)号：US09980649B1
公开(公告)日：2018-05-29
申请号：US15433588
申请日：2017-02-15
发明人： Mani Abedini , Rajib Chakravorty , Rahil Garnavi , Lenin Mehedy
CPC分类号： A61B5/0064 , A61B5/0077 , A61B5/441 , A61B5/444 , A61B5/70 , A61B34/30 , A61B90/35 , A61B90/50 , A61B2562/0233 , A61B2562/0242 , H04N5/2256 , H04N5/23203 , H04N7/181
摘要： A scan head for scanning skin includes a frame and a camera coupled to the frame. A controllable probe is coupled to the frame and is configured to change an orientation of hair on the skin to be examined and imaged with the camera.
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公开(公告)号：US20180122072A1
公开(公告)日：2018-05-03
申请号：US15859590
申请日：2017-12-31
CPC分类号： G06T7/0012 , G06K9/342 , G06K9/6223 , G06K9/6244 , G06K9/6249 , G06K2209/05 , G06T7/11 , G06T7/143 , G06T7/162 , G06T7/90 , G06T2207/10024 , G06T2207/20016 , G06T2207/20072 , G06T2207/20076 , G06T2207/20081 , G06T2207/30041 , G06T2207/30088 , G06T2207/30096 , G06T2207/30101
摘要： A structure-preserving composite model for skin lesion segmentation includes partitioning a dermoscopic image into superpixels at a first scale. Each superpixel is a vertex on a graph defined by color coordinates and spatial coordinates, and represents a number of pixels of the dermoscopic image according to the first scale. Further, constructing a plurality of k background templates by k-means clustering selected ones of the superpixels in space and color. Additionally, generating sparse representations of the plurality of superpixels based on the plurality of background templates. Also, calculating a reconstruction error for each superpixel by comparison of its sparse representation to its original color coordinates and spatial coordinates. Furthermore, outputting a confidence map that identifies each pixel of the dermoscopic image as belonging or not belonging to a skin lesion, based on the reconstruction errors of the representative superpixels.
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公开(公告)号：US20180121626A1
公开(公告)日：2018-05-03
申请号：US15336252
申请日：2016-10-27
CPC分类号： G16H50/30 , G06T7/0014 , G06T2207/20081 , G06T2207/30088 , G06T2207/30096 , G16H30/40 , G16H50/70
摘要： A method for risk assessment comprises receiving one or more images of a plurality of lesions captured from a body of a target person, generating one or more digital signatures based on the one or more images from the body of the target person, comparing the generated one or more digital signatures to digital signatures of respective reference persons, wherein the comparing comprises measuring similarities between the generated one or more digital signatures and the digital signatures of the respective reference persons, and determining a risk factor for the target person of developing a disease based on the measured similarities and predetermined risk factors of developing the disease for the reference persons.
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公开(公告)号：US09886758B2
公开(公告)日：2018-02-06
申请号：US15086988
申请日：2016-03-31
发明人： Mani Abedini , Rajib Chakravorty , Rahil Garnavi , Munawar Hayat
CPC分类号： G06T7/11 , G06K9/00147 , G06K9/4628 , G06K9/4638 , G06K9/622 , G06K2209/05 , G06T7/0012 , G06T7/12 , G06T7/136 , G06T7/149 , G06T7/187 , G06T2207/10056 , G06T2207/20081 , G06T2207/20084 , G06T2207/30088
摘要： A method for annotation of skin images includes receiving a plurality of dermatoscopic images. Each of the dermatoscopic includes a region of lesion skin and a region of normal skin. A first convolutional neural network is trained according to an interior of the region of lesion skin using each of the plurality of dermatoscopic images. A second convolutional neural network is trained according to a boundary between the region of lesion skin and the region of normal skin. An additional dermatoscopic image is acquired. The first and second convolutional neural networks are used to identify a region of lesion skin within the acquired additional dermatoscopic image.
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35.发明申请公开(公告)号：US20170351970A1
公开(公告)日：2017-12-07
申请号：US15175620
申请日：2016-06-07
摘要： Solar irradiation may be predicted based on input terrestrial sky images comprising cloud images, the terrestrial sky images taken from a plurality of geographic locations by a plurality of devices; for example, wherein the terrestrial sky images are crowd sourced from the plurality of devices. A model may be generated that predicts solar irradiation in a geographic area based on the input terrestrial sky images and the geographic locations from where the terrestrial sky images were taken. A signal representing the solar irradiation predicted by the model is output.
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公开(公告)号：US20170243345A1
公开(公告)日：2017-08-24
申请号：US15435654
申请日：2017-02-17
CPC分类号： G06T7/0012 , G06K9/342 , G06K9/6223 , G06K9/6244 , G06K9/6249 , G06K2209/05 , G06T7/11 , G06T7/143 , G06T7/162 , G06T7/90 , G06T2207/10024 , G06T2207/20016 , G06T2207/20072 , G06T2207/20076 , G06T2207/20081 , G06T2207/30041 , G06T2207/30088 , G06T2207/30096 , G06T2207/30101
摘要： A structure-preserving composite model for skin lesion segmentation includes partitioning a dermoscopic image into superpixels at a first scale. Each superpixel is a vertex on a graph defined by color coordinates and spatial coordinates, and represents a number of pixels of the dermoscopic image according to the first scale. Further, constructing a plurality of k background templates by k-means clustering selected ones of the superpixels in space and color. Additionally, generating sparse representations of the plurality of superpixels based on the plurality of background templates. Also, calculating a reconstruction error for each superpixel by comparison of its sparse representation to its original color coordinates and spatial coordinates. Furthermore, outputting a confidence map that identifies each pixel of the dermoscopic image as belonging or not belonging to a skin lesion, based on the reconstruction errors of the representative superpixels.
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公开(公告)号：US09684967B2
公开(公告)日：2017-06-20
申请号：US14921579
申请日：2015-10-23
发明人： Mani Abedini , Rajib Chakravorty , Rahil Garnavi , Munawar Hayat
CPC分类号： G06T7/13 , G06K9/4604 , G06K9/52 , G06K9/6256 , G06K9/6262 , G06K9/6267 , G06K9/6857 , G06T7/0012 , G06T7/10 , G06T7/11 , G06T7/143 , G06T7/90 , G06T2207/20016 , G06T2207/20021 , G06T2207/20076 , G06T2207/20081 , G06T2207/20112 , G06T2207/30088 , G06T2207/30096
摘要： A robust segmentation technique based on multi-layer classification technique to identify the lesion boundary is described. The inventors have discovered a technique based on training several classifiers such that to classify each pixel as lesion versus normal Each classifier is trained on a specific range of image resolutions. Then, for a new test image, the trained classifiers are applied on the image. Then by fusing the prediction results in pixel level a probability map is generated. In the next step, a thresholding method is applied to convert the probability map to a binary mask, which determines a mole border.
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38.发明申请CLOUD-BASED INFRASTRUCTURE FOR FEEDBACK-DRIVEN TRAINING AND IMAGE RECOGNITION 有权用于反馈驱动培训和图像识别的基于云的基础设施公开(公告)号：US20160171682A1
公开(公告)日：2016-06-16
申请号：US14738497
申请日：2015-06-12
CPC分类号： G06T7/0012 , G06F19/00 , G06F19/321 , G06K9/00147 , G06K9/6254 , G06K9/6263 , G06K2209/05 , G06K2209/053 , G06T2207/20081 , G06T2207/20092 , G06T2207/30048 , G06T2207/30088 , G16H50/20
摘要： A method for a cloud-based feedback-driven image training and recognition includes receiving a set of expert annotations of a plurality of training images of a predetermined subject matter, wherein the expert annotations include a clinical diagnosis for each image or region of interest in an image, training one or more classification models from the set of expert annotations, testing the one or more classification models on a plurality of test images that are different from the training images, wherein each classification model yields a clinical diagnosis for each image and a confidence score for that diagnosis, and receiving expert classification result feedback regarding the clinical diagnosis for each image and a confidence score yielded by each classification model.
摘要翻译： 一种用于基于云的反馈驱动图像训练和识别的方法包括接收预定主题的多个训练图像的一组专家注释，其中所述专家注释包括针对每个图像或感兴趣区域的临床诊断 图像，从所述专家注释集训练一个或多个分类模型，在与所述训练图像不同的多个测试图像上测试所述一个或多个分类模型，其中每个分类模型产生每个图像的临床诊断和置信度 对该诊断进行评分，并接收关于每个图像的临床诊断的专家分类结果反馈以及由每个分类模型产生的置信度得分。
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