The application discloses a kind of endometrial
carcinoma molecular typing methods based on cascaded multiple instance learning, method is by the binary
mask of tumor area generated to endometrial
carcinoma whole section image preprocessing, to extract the image block in
region of interest and construct instance set by
dyeing normalization
processing;Using PatchVMamba instance
encoder, the feature of each image block in instance set is extracted to obtain the instance
feature vector of each image block, and the instance feature bag of each whole section image is formed;Using attention mechanism, the instance feature bag is weighted and aggregated to obtain the slice-level feature representation;The slice-level feature is input into the three binary classifiers arranged in
cascade to obtain the probability value corresponding to the three subtypes of endometrial
carcinoma respectively, and a unified threshold is used for
cascade judgment of the
subtype classification of endometrial carcinoma to output the final
typing result.The cascaded structure of the application makes the consistency of the
typing result and the diagnosis of
pathological experts high, and realizes accurate
typing.