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Multi-Task Deep Learning With Dynamic Programming for Embryo Early Development Stage Classification From Time-Lapse Videos

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单位: [1]Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Minist Educ Image Proc & Intelligent Control, Key Lab, Wuhan 430074, Hubei, Peoples R China [2]Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Reprod Med Ctr, Wuhan 430074, Hubei, Peoples R China
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关键词: Multi-task learning in-vitro fertilization convolutional neural networks dynamic programming image classification

摘要:
Time-lapse is a technology used to record the development of embryos during in-vitro fertilization (IVF). Accurate classification of embryo early development stages can provide embryologists valuable information for assessing the embryo quality, and hence is critical to the success of IVF. This paper proposes a multi-task deep learning with dynamic programming (MTDL-DP) approach for this purpose. It first uses MTDL to pre-classify each frame in the time-lapse video to an embryo development stage, and then DP to optimize the stage sequence so that the stage number is monotonically non-decreasing, which usually holds in practice. Different MTDL frameworks, e.g., one-to-many, many-to-one, and many-to-many, are investigated. It is shown that the one-to-many MTDL framework achieved the best compromise between performance and computational cost. To our knowledge, this is the first study that applies MTDL to embryo early development stage classification from time-lapse videos.

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出版当年[2018]版:
大类 | 2 区 工程技术
小类 | 2 区 计算机:信息系统 3 区 工程:电子与电气 3 区 电信学
最新[2025]版:
大类 | 4 区 计算机科学
小类 | 4 区 计算机:信息系统 4 区 工程:电子与电气 4 区 电信学
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出版当年[2017]版:
Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Q1 TELECOMMUNICATIONS Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
最新[2023]版:
Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Q2 TELECOMMUNICATIONS

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第一作者单位: [1]Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Minist Educ Image Proc & Intelligent Control, Key Lab, Wuhan 430074, Hubei, Peoples R China
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