Patent ID: 11961618
Assignee: CITY UNIVERSITY OF HONG KONG
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. A method using a task-interaction network (TI-Net) for jointly performing a segmentation task to locate prostate cancer areas and a classification task to assess aggressiveness of lesions based on a multi parametric-magnetic resonance imaging (mp-MRI) scan image in terms of a plurality of defined categories, the TI-Net including a backbone network, an auxiliary segmentation branch, a classification branch and a main segmentation branch, the method comprising:
extracting, by the backbone network, an initial discriminative feature representation from an aligned and concatenated first and second type MRI slices of the mp-MRI scan image;
generating, by a probability mapping module in the auxiliary segmentation branch, an initial probability map based on the initial discriminative feature representation;
performing, by an auxiliary segmentation module in the auxiliary segmentation branch, a softmax operation on the initial probability map to obtain an initial lesion mask;
generating, by a lesion awareness module in the classification branch, a refined discriminative feature representation based on the initial lesion mask and the initial discriminative feature representation;
determining, by a prototyping module in the classification branch, a plurality of category prototypes corresponding to the plurality of defined categories respectively;
predicting, by a classification module in the classification branch, a lesion aggressiveness based on the refined discriminative feature representation and the plurality of category prototypes;
generating, by a category allocation module in the main segmentation branch, a hybrid feature representation based on the initial discriminative feature representation, the plurality of category prototypes and the predicted lesion aggressiveness; and
predicting, by a main segmentation module in the main segmentation branch, a lesion location based on the hybrid feature representation.