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Background and Objective: Bladder cancer is a common malignant urinarycarcinoma, with muscle-invasive and non-muscle-invasive as its two majorsubtypes. This paper aims to achieve automated bladder cancer invasivenesslocalization and classification based on MRI. Method: Different from previousefforts that segment bladder wall and tumor, we propose a novel end-to-endmulti-scale multi-task spatial feature encoder network (MM-SFENet) for locatingand classifying bladder cancer, according to the classification criteria of thespatial relationship between the tumor and bladder wall. First, we built abackbone with residual blocks to distinguish bladder wall and tumor; then, aspatial feature encoder is designed to encode the multi-level features of thebackbone to learn the criteria. Results: We substitute Smooth-L1 Loss with IoULoss for multi-task learning, to improve the accuracy of the classificationtask. By testing a total of 1287 MRIs collected from 98 patients at thehospital, the mAP and IoU are used as the evaluation metrics. The experimentalresult could reach 93.34\% and 83.16\% on test set. Conclusions: Theexperimental result demonstrates the effectiveness of the proposed MM-SFENet onthe localization and classification of bladder cancer. It may provide aneffective supplementary diagnosis method for bladder cancer staging.

MM-SFENet: Multi-scale Multi-task Localization and Classification of Bladder Cancer in MRI with Spatial Feature Encoder Network