MX2019000077A - Procedimiento y sistema para la clasificacion automatica de cromosomas. - Google Patents

Procedimiento y sistema para la clasificacion automatica de cromosomas.

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Publication number
MX2019000077A
MX2019000077A MX2019000077A MX2019000077A MX2019000077A MX 2019000077 A MX2019000077 A MX 2019000077A MX 2019000077 A MX2019000077 A MX 2019000077A MX 2019000077 A MX2019000077 A MX 2019000077A MX 2019000077 A MX2019000077 A MX 2019000077A
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MX
Mexico
Prior art keywords
sequence
chromosome
feature vectors
classification
crann
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MX2019000077A
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English (en)
Inventor
Vig Lovekesh
Sharma Monika
N/A Swati
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Tata Consultancy Services Ltd
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Publication date
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Publication of MX2019000077A publication Critical patent/MX2019000077A/es

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2431—Multiple classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G06T7/74—Determining position or orientation of objects or cameras using feature-based methods involving reference images or patches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/695—Preprocessing, e.g. image segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/698—Matching; Classification
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • Multimedia (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Medical Informatics (AREA)
  • Computational Linguistics (AREA)
  • Mathematical Physics (AREA)
  • Databases & Information Systems (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Genetics & Genomics (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Biotechnology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Image Analysis (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

La presente invención divulga un procedimiento y un sistema para la clasificación automática de cromosomas. El sistema, denominado alternativamente como Red Neuronal de Atención Recurrente Convolucional Residual (Res-CRANN), utiliza una propiedad de una secuencia de bandas de bandas cromosómicas para la clasificación de cromosomas. La Res-CRANN es un sistema entrenable de extremo a extremo, en el que se extrae una secuencia de vectores de características a partir de los mapas de características producidos por capas convolucionales de una Red Neuronal Residual (ResNet), en la que los vectores de características corresponden a características visuales que representan bandas cromosómicas en una imagen cromosómica. Los vectores de características de secuencia se alimentan en Redes Neuronales Recurrentes (RNN) aumentadas con un mecanismo de atención. La RNN aprende la secuencia de vectores de características y el módulo de atención se concentra en una pluralidad de regiones de interés (ROI) de la secuencia de vectores de características, en la que las ROI son específicas de una etiqueta de clase de cromosomas. La Res-CRANN proporciona una mayor precisión de clasificación en comparación con los procedimientos más modernos para la clasificación de cromosomas.
MX2019000077A 2018-07-06 2019-01-07 Procedimiento y sistema para la clasificacion automatica de cromosomas. MX2019000077A (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
IN201821025353 2018-07-06

Publications (1)

Publication Number Publication Date
MX2019000077A true MX2019000077A (es) 2020-01-07

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US (1) US10769408B2 (es)
EP (1) EP3591572B1 (es)
JP (1) JP6847910B2 (es)
CN (1) CN110689036B (es)
AU (1) AU2019200154B2 (es)
CA (1) CA3028669C (es)
MX (1) MX2019000077A (es)

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CN111402953B (zh) * 2020-04-02 2022-05-03 四川大学 基于层次注意力网络的蛋白质序列分类方法
CN111797834B (zh) * 2020-05-28 2021-06-15 华南理工大学 文本识别方法、装置、计算机设备和存储介质
CN111582409B (zh) * 2020-06-29 2023-12-26 腾讯科技（深圳）有限公司 图像标签分类网络的训练方法、图像标签分类方法及设备
CN111882001A (zh) * 2020-08-05 2020-11-03 武汉呵尔医疗科技发展有限公司 一种基于细胞生物学特征-卷积神经网络的宫颈细胞图像分类方法
CN112733873A (zh) * 2020-09-23 2021-04-30 浙江大学山东工业技术研究院 一种基于深度学习的染色体核型图分类方法及装置
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CN113256561B (zh) * 2021-04-21 2024-03-22 浙江工业大学 一种基于无归一化深度残差与注意力机制的肠道病灶辅助诊断方法
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CN113766544A (zh) * 2021-09-18 2021-12-07 国网河南省电力公司信息通信公司 一种基于多边缘协同的电力物联网切片优化方法
CN113724793B (zh) * 2021-11-01 2022-01-28 湖南自兴智慧医疗科技有限公司 基于卷积神经网络的染色体重要条带特征可视化方法及装置
CN113763382B (zh) * 2021-11-09 2022-02-11 常州微亿智造科技有限公司 工业质检中的检测装置
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EP3591572A1 (en) 2020-01-08
JP6847910B2 (ja) 2021-03-24
EP3591572B1 (en) 2021-09-01
AU2019200154B2 (en) 2020-10-08
AU2019200154A1 (en) 2020-01-23
CN110689036A (zh) 2020-01-14
JP2020009402A (ja) 2020-01-16
US20200012838A1 (en) 2020-01-09
CA3028669C (en) 2021-05-04
CN110689036B (zh) 2022-09-20
US10769408B2 (en) 2020-09-08
CA3028669A1 (en) 2020-01-06

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