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

TL;DR

This paper introduces MM-SFENet, an innovative end-to-end neural network that accurately localizes and classifies bladder cancer in MRI images by leveraging multi-scale features and a novel spatial feature encoding approach.

Contribution

The paper presents a novel multi-scale multi-task network with a spatial feature encoder for bladder cancer localization and classification, outperforming previous segmentation-based methods.

Findings

Achieved 93.34% mAP and 83.16% IoU on test data.

Substituting Smooth-L1 Loss with IoU Loss improved classification accuracy.

Validated effectiveness on 1287 MRI scans from 98 patients.

Abstract

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.