The invention provides a bone tumor fine-grained classification model training and classification method and device, and the method comprises the steps: constructing a multi-
modal positive sample containing global / local positive lateral X-
ray images,
lesion attributes and
patient information, and removing a false negative part in combination with text
semantic similarity to construct a high-quality
negative sample; global / local image features are extracted through double image encoders,
lesion attribute keywords are converted into'entity-translation-existence 'triples based on a
medical knowledge base, and basic information of a patient and global / local semantic features of
lesion attributes are extracted through a text
encoder; infoNCE contrast loss is constructed for global images and global
semantics based on contrast learning, global image-text feature alignment and local image-text feature alignment are realized in combination with a local
mutual information loss and classification loss training model calculated based on a DV variational formula, medical term
semantics are deeply combined, the training stability is improved, and the training efficiency is improved. The accuracy and robustness of bone tumor
subtype classification are remarkably improved, and reliable support is provided for clinical precise diagnosis.