Cancer prognosis survival prediction method, equipment and storage medium based on deep learning
A technology of deep learning and survival prediction, applied in the fields of computer technology, image analysis processing and clinical diagnosis, can solve the problems of low accuracy rate and achieve the effect of improving accuracy rate
Active Publication Date: 2021-10-19
安翰科技(武汉)股份有限公司
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[0003] At present, the prediction of the prognosis of cancer is generally only a diagnostic analysis of medical imaging data (such as pathological slice pictures), but in fact clinical data is also an important basis for clinical diagnosis. This method of unilateral data diagnosis and prediction , the accuracy rate is not high
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[0033] The present invention will be described in detail below in conjunction with specific embodiments shown in the accompanying drawings. However, these embodiments do not limit the present invention, and any structural, method, or functional changes made by those skilled in the art according to these embodiments are included in the protection scope of the present invention.
[0034] Such as figure 1 As shown, the present invention provides a method for predicting cancer prognosis and survival based on deep learning, and the method includes the following steps.
[0035] Step S100: Data Acquisition: Obtain sample data, which includes pathological image data and clinical data of the sample.
[0036] The step S100 is mainly used to collect information related to the prognosis of cancer patients. The information related to the prognosis of each cancer patient is a cancer sample data, and the sample data includes pathological image data and clinical data of the sample.
[0037]...
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The present invention discloses a method, device, and storage medium for predicting cancer prognosis and survival based on deep learning. The method includes: data acquisition: acquiring sample data, the sample data including pathological image data and clinical data of the sample; data preprocessing ; Training prediction model: train and evaluate the prediction model to obtain the best prediction model; risk prediction: perform risk prediction on new samples based on the optimal classifier model and the best prediction model. Compared with the prior art, the deep learning-based cancer prognosis and survival prediction method of the present invention unifies the data features of pathological image data and clinical data, and based on the unified data features of pathological image data and clinical data, the prediction model Conduct training and evaluation to obtain the optimal prediction model, and perform prognostic risk assessment on new sample data to improve the efficiency of diagnosis and treatment in this clinical field and the accuracy of risk assessment results.
Description
technical field [0001] The present invention belongs to the fields of computer technology, image analysis and processing, and clinical diagnosis, and specifically relates to artificial intelligence algorithms represented by machine learning and deep learning, as well as related technologies of clinical statistics, in particular to a method for predicting cancer prognosis and survival based on deep learning, equipment and storage media. Background technique [0002] Survival analysis refers to a series of statistical methods used to explore the occurrence of events of interest; unlike traditional regression problems, the research goal of survival analysis is the probability of an event occurring at a specific time point, and then estimates the survival of the object over time, while Not just predicting a target variable. Conventional survival analysis techniques include Kaplan-Meier (KM method) and Cox regression. The KM method is a non-parametric method to estimate the surv...
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IPC IPC(8): G16H50/20G16H50/70G06K9/62
CPCG16H50/20G16H50/70G06F18/241
Inventor 张楚康黄志威张皓明繁华
Owner 安翰科技(武汉)股份有限公司