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MM-SFENet: multi-scale multi-task localization and classification of bladder cancer in MRI with spatial feature encoder network.
- Source :
-
Physics in medicine and biology [Phys Med Biol] 2024 Jan 10; Vol. 69 (2). Date of Electronic Publication: 2024 Jan 10. - Publication Year :
- 2024
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Abstract
- 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. Approach . 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. Main Results . We substitute Smooth-L1 Loss with IoU Loss for multi-task learning, to improve the accuracy of the classification task. By learning two datasets collected from bladder cancer patients at the hospital, the mAP, IoU, Acc, Sen and Spec are used as the evaluation metrics. The experimental result could reach 93.34%, 83.16%, 85.65%, 81.51%, 89.23% on test set1 and 80.21%, 75.43%, 79.52%, 71.87%, 77.86% on test set2. Significance . 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.<br /> (© 2024 Institute of Physics and Engineering in Medicine.)
Details
- Language :
- English
- ISSN :
- 1361-6560
- Volume :
- 69
- Issue :
- 2
- Database :
- MEDLINE
- Journal :
- Physics in medicine and biology
- Publication Type :
- Academic Journal
- Accession number :
- 38091612
- Full Text :
- https://doi.org/10.1088/1361-6560/ad1548