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1.发明申请公开(公告)号：US20230033783A1
公开(公告)日：2023-02-02
申请号：US17806949
申请日：2022-06-15
发明人： Yoshihisa Shinagawa , Halid Yerebakan , Gerardo Hermosillo Valadez , Simon Allen-Raffl , Mahesh Ranganath , Michael Rusitska
摘要： There is disclosed a method and apparatus for annotating a first portion of medical imaging data with one or more words corresponding to a respective one or more features represented in the first portion of medical imaging data. A similarity metric indicating a degree of similarity between the first portion and each of a plurality of second portions of reference medical imaging data is determined, at least one of the plurality of second portions being annotated with one or more first words corresponding to a respective one or more features represented in the second portion. A second portion is selected based on the similarity metrics, and the first portion is annotated with the one or more first words with which the second portion, selected for the first portion, is annotated.
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公开(公告)号：US11430119B2
公开(公告)日：2022-08-30
申请号：US17017914
申请日：2020-09-11
摘要： A method and for quantifying a three-dimensional medical image volume are provided. An embodiment of the method includes: providing a two-dimensional representation image based on the medical image volume; defining a region of interest in the two-dimensional representation image; generating a feature signature for the region of interest; defining a plurality of two-dimensional image patches in the medical image volume; calculating, for each of the image patches, a degree of similarity between the region of interest and the respective image patch on the basis of the feature signature; visualizing the degrees of similarities.
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公开(公告)号：US20200334410A1
公开(公告)日：2020-10-22
申请号：US16846756
申请日：2020-04-13
IPC分类号： G06F40/126 , G06F40/284 , G06F40/205 , G06F40/58
摘要： A computer-implemented method of encoding a word for use in a method of text analysis comprises receiving input text to be analysed, the input text comprising a first word which is not represented in a vocabulary set stored on a storage. The vocabulary set comprises a plurality of words and an associated word embedding vector for each word in the set. The method comprises identifying the first word as a word which is not represented in the vocabulary set and determining one or more sub-words within the first word with which to encode the first word. Each of the one or more sub-words corresponds with a word represented in the vocabulary set and having an embedding vector in the vocabulary set. The method comprises determining an encoding for the first word based on the one or more sub-words.
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公开(公告)号：US10685438B2
公开(公告)日：2020-06-16
申请号：US16016776
申请日：2018-06-25
发明人： Fitsum Aklilu Reda , Yiqiang Zhan , Parmeet Singh Bhatia , Yoshihisa Shinagawa , Luca Bogoni , Xiang Sean Zhou
IPC分类号： G06K9/00 , G06T7/00 , G01B21/20 , G06T7/12 , G06T7/60 , G06T7/66 , G06N20/00 , G06K9/62 , G06T7/62 , G06K9/46
摘要： A framework for automated measurement. In accordance with one aspect, the framework detects a centerline point of a structure of interest in an image. A centerline of the structure of interest may be traced based on the detected centerline point. A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours.
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公开(公告)号：US20230041553A1
公开(公告)日：2023-02-09
申请号：US17806159
申请日：2022-06-09
发明人： Yoshihisa Shinagawa , Halid Yerebakan , Gerardo Hermosillo Valadez , Simon Allen-Raffl , Mahesh Ranganath
摘要： A computer implemented method and apparatus determines a body region represented by medical imaging data stored in a first image file. The first image file further stores one or more attributes each having an attribute value comprising a text string indicating content of the medical imaging data. One or more of the text strings of the first image file are obtained and input into a trained machine learning model, the machine learning model having been trained to output a body region based on an input of one or more such text strings. The output from the trained machine learning model is obtained thereby to determine the body region represented by the medical imaging data. Also disclosed are methods of selecting one or more sets of second medical imaging data as relevant to first medical imaging data.
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公开(公告)号：US20220398374A1
公开(公告)日：2022-12-15
申请号：US17713673
申请日：2022-04-05
IPC分类号： G06F40/166 , G06F40/30 , G06F40/279 , G06F40/123 , G16H15/00 , G06N3/08
摘要： A framework for segmenting a medical text report into sections is disclosed. For each sentence of the report, a first sentence representation is determined by inputting a word-level context representation for each sentence sequentially into a neural network. A second sentence representation is determined by inputting an aggregated representation for each sentence sequentially into another neural network. For each sentence, a third sentence representation is determined based on a combination of the first and second sentence representations, and a section classification for the sentence is determined by inputting the third sentence representation into a section classifier. Each sentence is assigned the section classification determined for the sentence.
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公开(公告)号：US11176188B2
公开(公告)日：2021-11-16
申请号：US15865539
申请日：2018-01-09
IPC分类号： G06F16/00 , G06F16/35 , G16H50/70 , G06K9/00 , G06N20/00 , G06F16/93 , G06K9/62 , G06F16/36 , G16H15/00 , G16H30/40 , G06N3/04 , G06N3/08 , G06N7/00 , G16H10/60 , G06F40/30 , G06N20/10 , G06N5/00
摘要： A visualization framework based on document representation learning is described herein. The framework may first convert a free text document into word vectors using learning word embeddings. Document representations may then be determined in a fixed-dimensional semantic representation space by passing the word vectors through a trained machine learning model, wherein more related documents lie closer than less related documents in the representation space. A clustering algorithm may be applied to the document representations for a given patient to generate clusters. The framework then generates a visualization based on these clusters.
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公开(公告)号：US20190019287A1
公开(公告)日：2019-01-17
申请号：US16016776
申请日：2018-06-25
发明人： Fitsum Aklilu Reda , Yiqiang Zhan , Parmeet Singh Bhatia , Yoshihisa Shinagawa , Luca Bogoni , Xiang Sean Zhou
CPC分类号： G06T7/0012 , G01B21/20 , G06K9/4628 , G06K9/627 , G06K2209/051 , G06N20/00 , G06T7/12 , G06T7/60 , G06T7/62 , G06T7/66 , G06T2200/24 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/30048 , G06T2207/30101 , G06T2207/30172
摘要： A framework for automated measurement. In accordance with one aspect, the framework detects a centerline point of a structure of interest in an image. A centerline of the structure of interest may be traced based on the detected centerline point. A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours.
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公开(公告)号：US20240005493A1
公开(公告)日：2024-01-04
申请号：US18312822
申请日：2023-05-05
IPC分类号： G06T7/00
CPC分类号： G06T7/0012 , G06T2207/20
摘要： A framework for identifying a type of organ in a volumetric medical image. The framework may include receiving a volumetric medical image, the volumetric medical image comprising at least one organ or portion thereof, and further receiving a single point of interest within the volumetric medical image. Voxels are sampled from the volumetric medical image, wherein at least one voxel is skipped between two sampled voxels. The type of organ is identified at the single point of interest by applying a trained classifier to the sampled voxels.
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公开(公告)号：US20230005136A1
公开(公告)日：2023-01-05
申请号：US17806156
申请日：2022-06-09
发明人： Halid Yerebakan , Gerardo Hermosillo Valadez , Yoshihisa Shinagawa , Matthias Wolf , Anna Jerebko , Yu Zhao , Simon Allen-Raffl , Katharina Schmidler Burk , Mahesh Ranganath
摘要： A computer implemented method and apparatus for determining a location at which a given feature is represented in medical imaging data is disclosed. A first descriptor for a first location in first medical imaging data is obtained. The first location is the location within the first medical imaging data at which the given feature is represented. A second descriptor for each of a plurality of candidate second locations in second medical imaging data is obtained. A similarity metric indicating a degree of similarity with the first descriptor is calculated for each of the plurality of candidate second locations. A candidate second location is selected from among the plurality of candidate second locations based on the calculated similarity metrics. The location at which the given feature is represented in the second medical imaging data is determined based on the selected candidate second location.
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