Patent ID: 11963788
Assignee: CITY UNIVERSITY OF HONG KONG
Field: Medical technology (Instruments)
Classification: CPC A  G | IPC A  G

Claim 3:
4. A method for training a graph-based prostate diagnosis network including a feature extractor, a pooling operator, a preliminary classifier, an instance importance calculator, a plurality of importance-guided graph (IGraph) layers, an embedding aggregator and a refined classifier, the method comprising:
preparing a training dataset of 3D magnetic resonance imaging (MRI) scans, each having a patient-level label ŷ and containing a plurality of 2D MRI slices;
for each of the 3D MRI scans:
feeding the 3D MRI scan to the feature extractor and training the feature extractor, to extract a plurality of preliminary instance embeddings Z corresponding to a plurality of 2D MRI slices of the fed 3D MRI scan respectively;
feeding the plurality of preliminary instance embeddings Z to the pooling operator and training the pooling operator to aggregate the plurality of preliminary instance embeddings Z together to generate a preliminary bag embedding zbag;
feeding the preliminary bag embedding zbag to the preliminary classifier, and training the preliminary classifier to:
process the preliminary bag embedding zbag to generate a preliminary diagnosis prediction ypre;
calculate an instance importance parameter w based on the preliminary bag embedding zbag; and
adjust the instance importance parameter w under supervision with a loss function measuring a difference between the preliminary diagnosis prediction and a patient-level label corresponding to the fed 3D MRI scan;

feeding the instance importance parameter w and the plurality of preliminary instance embeddings Z to the instance importance calculator and training the instance importance calculator to calculate a plurality of instance importances a corresponding to the plurality of preliminary instance embeddings respectively based on the instance importance parameter w;
feeding the plurality of preliminary instance embeddings Z to the plurality of IGraph layers and training the plurality of IGraph layers to generate a plurality of improved instance embeddings {tilde over (Z)} by performing a plurality of graph convolutions on the plurality of preliminary instance embeddings Z in a sequential manner;
feeding the plurality of improved instance embeddings {tilde over (Z)} and the plurality of instance importances a to the embedding aggregator and training the embedding aggregator to generate an improved bag embedding zI by aggregating the plurality of improved instance embeddings {tilde over (Z)} with the plurality of instance importances a;
feeding the improved bag embedding zI to the refined classifier and training the refined classifier to process the improved bag embedding zI to generate a refined diagnosis prediction y under supervision with a loss function measuring a difference between the refined diagnosis prediction and the patient-level label corresponding to the fed 3D MRI scan.