Hepatocellular carcinoma CK19 and MVI prediction method based on multi-task learning
The invention discloses a hepatocellular carcinoma CK19 and MVI prediction method based on multi-task learning, and the method comprises the steps: controlling a first feature extraction module, a second feature extraction module and a third feature extraction module to determine a first feature map...
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| creator | FENG SHITING LONG TINGYU WANG JIFEI HUANG BINGSHENG CHEN JIAZHAO DONG ZHI CHEN JIA ZHOU XIAOQI PENG ZHENPENG CHEN YUYING WANG MENG LIN CHUXUAN |
| description | The invention discloses a hepatocellular carcinoma CK19 and MVI prediction method based on multi-task learning, and the method comprises the steps: controlling a first feature extraction module, a second feature extraction module and a third feature extraction module to determine a first feature map, a second feature map and a third feature map based on a to-be-predicted MR image; the control prediction module determines a CK19 expression category and an MVI category of the MR image based on the first feature map, the second feature map, and the third feature map. According to the method, rich image features carried by the to-be-predicted MR image are directly extracted through the prediction network model, and the problem that the prediction performance of the model is affected by subjectivity during image feature analysis can be avoided. Meanwhile, CK19 expression features are extracted through a first feature extraction module, CK19 expression and MVI shared features are extracted through a second feature |
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According to the method, rich image features carried by the to-be-predicted MR image are directly extracted through the prediction network model, and the problem that the prediction performance of the model is affected by subjectivity during image feature analysis can be avoided. 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According to the method, rich image features carried by the to-be-predicted MR image are directly extracted through the prediction network model, and the problem that the prediction performance of the model is affected by subjectivity during image feature analysis can be avoided. 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According to the method, rich image features carried by the to-be-predicted MR image are directly extracted through the prediction network model, and the problem that the prediction performance of the model is affected by subjectivity during image feature analysis can be avoided. Meanwhile, CK19 expression features are extracted through a first feature extraction module, CK19 expression and MVI shared features are extracted through a second feature</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | Hepatocellular carcinoma CK19 and MVI prediction method based on multi-task learning |
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