CN116934675A - Plateau pulmonary edema prediction method based on multi-feature fusion and contrast learning - Google Patents

Plateau pulmonary edema prediction method based on multi-feature fusion and contrast learning Download PDF

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CN116934675A
CN116934675A CN202310433513.0A CN202310433513A CN116934675A CN 116934675 A CN116934675 A CN 116934675A CN 202310433513 A CN202310433513 A CN 202310433513A CN 116934675 A CN116934675 A CN 116934675A
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pulmonary edema
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高全胜
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Environmental Medicine and Operational Medicine Institute of Military Medicine Institute of Academy of Military Sciences
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Abstract

The invention discloses a predictive diagnosis method for altitude pulmonary edema, which comprises the following steps: s1, a full-automatic focus segmentation algorithm based on Dense-U-Net; s2, constructing a two-branch feature learning network, and learning focus and full lung features; s3, constructing a sample pair; s4, comparing feature learning; s5, predicting plateau pulmonary edema. The convolution neural network model based on multi-feature fusion and contrast learning can be used for analyzing CT images of the plateau pulmonary edema in a full-automatic mode, and further prediction of the plateau pulmonary edema is achieved. Compared with the method for classifying by using a single convolutional neural network, the method uses multi-feature fusion and feature contrast learning, randomly selects two samples to construct a sample pair during training, and then uses a network sharing weights to extract the full lung features and focus features of the two samples in the sample pair; the method comprises the steps of measuring the distance between features of two samples in a feature contrast learning mode, wherein if the two samples belong to the same prediction, the distance between the whole lung features and focus features of the two samples is relatively short; if the two samples belong to different predictions, the lung features and lesion features of the two samples are respectively farther apart. By combining the feature distance and the cross entropy loss function, the deep learning features of the samples of the same category are similar, the deep learning features of the samples of different categories are large in difference, the features are optimized and fused, and the classification precision is improved.

Description

Plateau pulmonary edema prediction method based on multi-feature fusion and contrast learning
Technical Field
The invention relates to a medical technology, in particular to a plateau pulmonary edema prediction method based on multi-feature fusion and contrast learning.
Background
In high altitude areas, altitude sickness, including altitude pulmonary edema, is easily caused by insufficient oxygen supply to the body due to rarefaction of oxygen. Altitude pulmonary edema is a serious pulmonary disease with high morbidity and mortality. Traditional diagnostic methods for altitude pulmonary edema typically rely on manual observation and analysis of CT images of the lungs. However, this method has problems such as complicated operation and low accuracy. In recent years, with the development of deep learning technology, automatic analysis and diagnosis of lung images by using convolutional neural networks are becoming a new research direction.
However, there are some limitations to the automatic analysis and prediction of lung images using conventional convolutional neural networks, such as limitations in feature extraction, differences between different cases, and the like. Therefore, researchers have proposed a predictive diagnosis method for altitude pulmonary edema based on multi-feature fusion and contrast learning. The method adopts a Dense-U-Net model to carry out full-automatic focus segmentation, and builds a two-branch characteristic learning network to learn focus and full-lung characteristics. During training, two samples are randomly selected to construct a sample pair, the two samples in the sample pair are subjected to full-lung feature extraction and focus feature extraction by using a network sharing weights, and then the features of the two samples are subjected to distance measurement in a contrast learning mode. By combining the feature distance and the cross entropy loss function, the method can enable the deep learning features of samples of the same category to be similar, and the deep learning features of samples of different categories have large difference, so that the features are optimized, and the classification precision is improved. Compared with the traditional convolutional neural network method, the method has better diagnosis precision and higher automation degree.
Disclosure of Invention
The invention discloses a predictive diagnosis method for altitude pulmonary edema based on multi-feature fusion and contrast learning. The method uses the convolutional neural network model to carry out full-automatic analysis and classification on the CT image of the altitude pulmonary edema, and can realize the prediction of the altitude pulmonary edema.
Specifically, the method comprises the following steps:
s1, a full-automatic focus segmentation algorithm based on Dense-U-Net is used for automatically segmenting focus areas in CT images of plateau pulmonary edema;
s2, constructing two branch feature learning networks which are respectively used for learning the features of focus and whole lung, and improving the classification accuracy by a multi-feature fusion mode;
s3, constructing a sample pair, and adopting a mode of randomly selecting two samples to construct the sample pair for training a network to perform feature learning and contrast learning;
s4, comparing feature learning, namely performing full-lung feature extraction and focus feature extraction on two samples in a sample pair in a feature comparison learning mode, and performing distance measurement on the two samples in a feature distance mode; if the two samples belong to the same label, the whole lung characteristics and focus characteristics of the two samples are respectively closer in distance; if the two samples belong to different labels, the lung features and focus features of the two samples are far away respectively;
s5, plateau pulmonary edema prediction
After the model is trained, one network in the twin network in the step S4 is taken out, and the prediction of unknown plateau pulmonary edema CT images is realized.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a schematic diagram of a full-automatic lung segmentation algorithm based on Dense-U-Net;
FIG. 2 is a flow chart of multi-feature fusion and contrast feature learning.
Detailed Description
The present invention will be further described with reference to the accompanying drawings, and it should be noted that, while the present embodiment provides a detailed implementation and a specific operation process on the premise of the present technical solution, the protection scope of the present invention is not limited to the present embodiment.
The invention comprises the following steps:
s1, a full-automatic focus segmentation algorithm based on Dense-U-Net. Focus segmentation is carried out on the CT image of the plateau pulmonary edema by using a Dense-U-Net algorithm, and focus areas in the image are automatically segmented.
S2, constructing a two-branch feature learning network, learning focus and whole lung features, and designing a feature fusion algorithm. The full lung characteristics and focus characteristics output by Dense-U-Net are respectively input into the characteristic learning network of the two branches, the characteristic representations of the two branches are learned, and when the characteristics are fused, in order to remove redundant information and enhance key parts, the importance of each characteristic is weighted by using an attention mechanism.
The whole lung is characterized byThe focus is characterized by->The weighted result of the two features after the attention mechanism can be expressed as:
f′ 1 ＝f 1 ·softmax(Conv(ReLU(BN(Conv(f 1 )))))
f′ 2 ＝f 2 ·softmax(Conv(ReLU(BN(Conv(f 2 )))))
where Conv represents a convolution operation, BN represents a batch normalization operation, reLU represents an activation function, and softmax represents a softmax function that normalizes the attention coefficient. Attention factor, i.e. softmax (Conv (ReLU (BN (Conv (f)))), where f may be f 1 Or f 2 。
Will weightSplicing the features to obtain a fused feature f fuse ：
Wherein, the liquid crystal display device comprises a liquid crystal display device,representing the stitching operation of tensors, alpha represents the weight of the whole lung feature, and 1-alpha represents the weight of the lesion feature. h, w and c respectively represent the height, width and channel number of the feature map after splicing, and the key part after feature fusion can be controlled by adjusting the splicing proportion.
S3, constructing a sample pair. In the training process, two samples are randomly selected to construct a sample pair so as to perform feature contrast learning.
S4, comparing feature learning. The method comprises the steps of carrying out full-lung feature extraction and focus feature extraction on two samples in a sample pair by using a network sharing weights, carrying out distance measurement on features of the two samples in a feature comparison learning mode, and if the two samples belong to the same label, respectively enabling the full-lung features and focus features of the two samples to be closer in distance; if the two samples belong to different labels, the lung features and lesion features of the two samples are respectively farther apart. The model is optimized through the contrast characteristic loss function, and a loss function formula of contrast characteristic learning can be expressed as:
wherein Y is a binary variable of whether the categories of the two samples are the same, w is a weight parameter, A and B are feature vectors of the two samples, D is a distance metric function, and m is a margin parameter.
S5, predicting plateau pulmonary edema. Through a feature fusion algorithm, redundant feature screening important features can be removed, and the correlation between the features and a final task is improved. By combining the feature distance and the cross entropy loss function, the deep learning features of samples of the same category can be similar, the deep learning features of samples of different categories are large in difference, the features are optimized, and the classification precision is improved. Predictive diagnosis of plateau pulmonary edema can be made by predicting predictions for each sample.
After the model is trained, one of the twin networks in step S4 is taken out as a plateau pulmonary edema prediction model. When the unknown plateau pulmonary edema CT image is predicted, the lung image and focus image (obtained in step S1) of the CT image are input into the model to obtain the prediction result of the sample.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting it, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that: the technical scheme of the invention can be modified or replaced by the same, and the modified technical scheme cannot deviate from the spirit and scope of the technical scheme of the invention.

Claims (1)

1. A plateau pulmonary edema prediction method based on multi-feature fusion and contrast learning is characterized by comprising the following steps: the method comprises the following steps:
s1, constructing a full-automatic focus segmentation network based on Dense-U-Net: a U-shaped full convolution network (Dense-U-Net) based on DenseNet extracts features by downsampling, upsamples to generate segmentation results, and fully automatically segments focal regions from CT images.
S2, constructing a feature learning network: extracting the characteristics of a lung CT image and a focus image by using a three-dimensional DenseNet121 as a characteristic extraction network;
s3, sample pair construction: in a training set, randomly extracting two samples each time to construct a sample pair, if the two samples have the same label, marking the label of the sample pair as 1, and if the two samples have different labels, marking the label of the sample pair as-1;
s4, contrast characteristic learning: and (3) constructing two branch twin networks based on the feature extraction network in the step (S2), wherein weight sharing among the twin networks is respectively used for extracting various features of two samples in the sample pair in the step (S3). Then, providing a contrast feature loss function, measuring the distances of various features of two samples, so that the feature distances among the same type of samples are similar, and the feature distances among different types of samples are far; and combining the cross entropy loss function to realize the contrast characteristic learning of the model.
S5, predicting plateau pulmonary edema: after the model is trained, one network in the twin network in the step S4 is taken out, and the prediction of unknown plateau pulmonary edema CT images is realized.
CN202310433513.0A 2023-04-21 2023-04-21 Plateau pulmonary edema prediction method based on multi-feature fusion and contrast learning Pending CN116934675A (en)

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