Background and Objective: Bladder cancer is a common malignant urinary carcinoma, with muscle-invasive and non-muscle-invasive as its two major subtypes. This paper aims to achieve automated bladder cancer invasiveness localization and classification based on MRI. Method: Different from previous efforts that segment bladder wall and tumor, we propose a novel end-to-end multi-scale multi-task spatial feature encoder network (MM-SFENet) for locating and classifying bladder cancer, according to the classification criteria of the spatial relationship between the tumor and bladder wall. First, we built a backbone with residual blocks to distinguish bladder wall and tumor; then, a spatial feature encoder is designed to encode the multi-level features of the backbone to learn the criteria. Results: We substitute Smooth-L1 Loss with IoU Loss for multi-task learning, to improve the accuracy of the classification task. By testing a total of 1287 MRIs collected from 98 patients at the hospital, the mAP and IoU are used as the evaluation metrics. The experimental result could reach 93.34\% and 83.16\% on test set. Conclusions: The experimental result demonstrates the effectiveness of the proposed MM-SFENet on the localization and classification of bladder cancer. It may provide an effective supplementary diagnosis method for bladder cancer staging.

翻译：背景与目的：膀胱癌是一种常见的恶性泌尿系肿瘤，其两种主要亚型为肌层浸润性与非肌层浸润性。本文旨在基于MRI实现膀胱癌侵袭性的自动定位与分类。方法：不同于以往分割膀胱壁与肿瘤的研究，我们根据肿瘤与膀胱壁空间关系的分类标准，提出了一种新颖的端到端多尺度多任务空间特征编码网络（MM-SFENet），用于膀胱癌的定位与分类。首先，我们构建了带有残差块的骨干网络以区分膀胱壁与肿瘤；随后，设计了空间特征编码器对骨干网络的多层级特征进行编码，以学习分类标准。结果：我们采用IoU损失替代Smooth-L1损失进行多任务学习，以提高分类任务的精度。通过在医院收集的98例患者共1287张MRI图像进行测试，以平均精度（mAP）和交并比（IoU）作为评估指标。在测试集上，实验结果可达93.34%和83.16%。结论：实验结果证明了所提出的MM-SFENet在膀胱癌定位与分类中的有效性，可为膀胱癌分期提供有效的辅助诊断方法。