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Exosome electron microscope picture judgment system and method based on deep learning

A technology of deep learning and exosomes, which is applied in the direction of instruments, biological neural network models, character and pattern recognition, etc., can solve the problems of huge manpower and material resources, and the resistance of exosome technology to clinical practice, and achieve the goal of reducing professional difficulty Effect

Pending Publication Date: 2020-04-07
SHANGHAI SIXTH PEOPLES HOSPITAL
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Problems solved by technology

In fact, it takes a lot of manpower and material resources to organize such a large-scale training and assessment, which will inevitably cause greater resistance to the promotion of exosome technology to the clinic

Method used

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  • Exosome electron microscope picture judgment system and method based on deep learning
  • Exosome electron microscope picture judgment system and method based on deep learning

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Embodiment Construction

[0037] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be further described below.

[0038] Such as figure 1 As shown, the present invention proposes an exosome electron microscope picture judgment system based on deep learning, including a client module, a judgment module, a feedback module, an expert module and a training module;

[0039] Among them, the client module is the client APP software, and the user can download the APP through the network mobile device to realize the import of the picture. The client module is mainly used for the user to transmit the picture to the judgment module, and to receive and feedback the picture judgment result output by the judgment module ;

[0040] The judgment module is a convolutional neural network, named: Exo-ConvNet, Exo-ConvNet includes an output layer, an input layer and multiple hidden layers;

[0041] The input layer convert...

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Abstract

The invention provides an exosome electron microscope picture judgment system and method based on deep learning so that simple and reliable identification of an exosome TEM photo can be realized; anda training set of a training unit is continuously perfected by receiving the feedback information of a user, so that most of the user can easily and visually judge the obtained TEM photo without professional training, and the professional difficulty and the invested labor cost are greatly reduced.

Description

technical field [0001] The present invention relates to the technical field of exosome judgment, in particular to a deep learning-based exosome electron microscope picture judgment system and judgment method. Background technique [0002] Exosomes are cell-derived extracellular vesicles (Extracellular vesicles) with a diameter of 30-150 nm. In recent years, exosomes have made a lot of breakthroughs in the fields of disease pathogenesis, liquid biopsy and tissue regeneration, and are expected to become a new tool for understanding the law of disease occurrence and development, early diagnosis of diseases and regenerative medicine. [0003] The current identification methods of exosomes include particle size distribution measurement, electron microscope observation and immunological marker detection. Among them, electron microscope observation, especially transmission electron microscope (Transmission electron microscope, TEM) observation is the most intuitive method among al...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/214
Inventor 陶诗聪郭尚春
Owner SHANGHAI SIXTH PEOPLES HOSPITAL
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