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Medical image annotation recommendation method based on block level active learning

A medical image and active learning technology, applied in the field of image annotation, can solve problems such as repeated recommendations

Active Publication Date: 2019-09-10
ZHEJIANG UNIV
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AI Technical Summary

Problems solved by technology

The present invention solves the problem of repeated recommendation on medical images by existing labeling recommendation methods by locating valuable labeling regions

Method used

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  • Medical image annotation recommendation method based on block level active learning
  • Medical image annotation recommendation method based on block level active learning
  • Medical image annotation recommendation method based on block level active learning

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Embodiment Construction

[0059] The method of the present invention will be further described below in conjunction with the accompanying drawings.

[0060] Need to do following preparations in the early stage of the inventive method:

[0061] 1) Build a semantic segmentation network based on deep learning: Any semantic segmentation method based on a deep neural network is applicable. Such as figure 1 As shown, a specific semantic segmentation network constructed in this example is as follows:

[0062] a) Define the basic structure of the composite component, which consists of the following components stacked in order: ω 3×3 convolution kernels, a batch normalization layer (BatchNorm), a ReLU activation layer, ω 3×3 convolution kernels , a batch normalization layer, and a ReLU activation layer; where ω is the parameter of the composite component, indicating the number of convolution kernels, and the composite component is recorded as Block(ω);

[0063] b) The semantic segmentation network consists o...

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Abstract

The invention discloses a medical image annotation recommendation method based on block-level active learning, which comprises the following steps: firstly, dividing a whole image into different regions, identifying and distinguishing the types of objects contained in each region, and then annotating and recommending image blocks and object types to realize fine-grained evaluation of annotation values of each region of the image. According to the method, the region with the annotation value is positioned, so that the problem of repeated recommendation on the medical image in an existing annotation recommendation method is solved. According to the method, the basic unit of image annotation recommendation is reduced to the image block level, resource waste caused by repeated annotation of similar objects in the image is avoided, and the annotation cost is further reduced. Compared with the current best medical image annotation recommendation method, the method has the advantages that under the condition that the same semantic segmentation precision is achieved, the annotation cost can be reduced by 15% at most, or under the condition that the same annotation cost is achieved, the semantic segmentation precision can be improved by 2%.

Description

technical field [0001] The invention belongs to the technical field of image labeling methods, and relates to a block-level active learning-based medical image labeling recommendation method. Background technique [0002] In the field of computer vision, semantic segmentation is a very basic and challenging task, which aims to distinguish and locate each pre-defined object on a picture at the pixel level. For example, in an autonomous driving scenario, it is hoped that different targets such as cars, pedestrians, roads, and signal lights in the current vehicle monitoring screen can be distinguished through semantic segmentation methods, and their locations can be determined so that the control system can make subsequent decisions. With the advent of the era of big data, the development of computer vision technology is changing with each passing day. Compared with the traditional methods of "standing in place", a series of data-driven methods represented by deep neural networ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06T7/11
CPCG06T7/11G06F18/23213G06F18/2415
Inventor 尹建伟林博张金迪邓水光李莹方维佳张鹿鸣尚永衡
Owner ZHEJIANG UNIV
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