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:背景和目标:刀形癌症是一种常见恶性尿囊癌,其两个主要子类型是肌肉侵入和非肌肉侵入性肿瘤,作为两大子类型。本文旨在实现基于MRI的膀胱癌入侵局部化和分类自动化膀胱癌。方法:不同于以前分割膀胱墙和肿瘤的努力,我们提议建立一个新型端到端多级多任务空间特征编码网络(MM-SFENet),根据肿瘤和膀胱墙之间的空间关系的分类标准,确定和分类膀胱癌。首先,我们用残余块建立了骨干,以区分膀胱墙和肿瘤;然后,设计了一个空间特征编码器,以编码骨骼的多层次特征,以学习标准。结果:我们用液态-L1损失替代多任务学习的IoU损失,以提高分类任务的准确性。通过测试从医院98名病人收集的总共1287个MIS,将MAP和IoU用作评价指标。实验结果可以达到93.34 ⁇ 和83.16个空间特征编码器,以输入骨质骨质骨质的多级特征,以学习标准。结果:我们用SISULU损失的实验性分析方法可以提供癌症的测试结果。