Similarity determining method and device, network training method and device, search method and device, and electronic device and storage medium

A training method of a neural network used for determining a data similarity, comprising:

• acquiring first training data of a training object, wherein the first training data comprises first training sub-data of a first modality and second training sub-data of a second modality, the training object comprises a training characteristic, the first training sub-data comprises a first training sub-semantic describing the training characteristic, and the second training sub-data comprises a second training sub-semantic describing the training characteristic;
• mapping the first training sub-data as a first training semantic representation in a semantic comparison space through a first neural network, wherein the semantic comparison space enables a similarity between a semantic representation obtained by mapping data of the first modality to the semantic comparison space and a semantic representation obtained by mapping data of the second modality to the semantic comparison space to be computed;
• mapping the second training sub-data as a second training semantic representation in the semantic comparison space through a second neural network;
• and
• calculating a similarity between the first training semantic representation and the second training semantic representation, and adjusting parameters of at least one of the first neural network and the second neural network according to the similarity between the first training semantic representation and the second training semantic representation.

A training method of a neural network used for a patient similarity determination method, comprising:

• acquiring medical text data and medical image data of a training patient, wherein both the medical image data of the training patient and the medical text data of the training patient comprise a training sub-semantic describing a training characteristic of the training patient;
• mapping the medical text data of the training patient as a first training semantic representation in a semantic comparison space through a first neural network, wherein the semantic comparison space enables a similarity between a semantic representation obtained by mapping data of a medical text to the semantic comparison space and a semantic representation obtained by mapping data of a medical image to the semantic comparison space to be computed;
• mapping the medical image data of the training patient as a second training semantic representation in the semantic comparison space through a second neural network;
• and
• calculating a similarity between the first training semantic representation and the second training semantic representation, and adjusting parameters of at least one selected from a group comprising the first neural network and the second neural network according to the similarity between the first training semantic representation and the second training semantic representation.