Abstract
Bladder cancer is a type of commonly encountered clinical malignant tumors of the urinary system with high recurrence and high mortality. Recent studies have suggested that the non-invasive and accurate classification of bladder cancer using MRI images and deep learning models is of great significance to formulating treatment plans and evaluating prognosis. However, due to various kinds of difficulties in data collection, medical imaging data often have the characteristics of small samples, incomplete sequence data, and mixed storage of all case data. To address these issues, we propose in this paper a multi-granularity intelligent diagnostic method for staging and grading bladder cancer using limited MRI data, which automatically divides the case data, and adaptively selects data augmentation methods and deep learning model according to the number of case data. For the diagnostic model of sequence image data, the autoencoder based model is used to optimize the diagnostic features of the images, and the feature correlation between data sequences is used to classify bladder cancer. For the diagnostic model of single image data, the attention mechanism is introduced to strengthen the diagnostic features of images. Experiments show that this method has an accuracy rate of 95.72% for grading of bladder cancer, and an accuracy rate of 88.57% for staging.
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Data availability
The data set we use is the bladder cancer data set provided by the 2019 China University Computer Design Competition.
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Funding
This work was partially supported by Project for Research on Medical in Jiangsu Commission of Health [through grant Z2020032], by National Natural Science Foundation of China [through grant No.62102345], by The Youth Medical Science and Technology Innovation Project of Xuzhou Municipal Health Commission [through grant XWKYHT20210586], and by Special support for "Young Talents" of the Affiliated Hospital of Xuzhou Medical University [through grant 2020QQMRC08].
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Chen, X., Xu, Q., Xu, H. et al. Intelligent diagnosis of bladder cancer with limited MRI data. J Ambient Intell Human Comput 14, 13729–13740 (2023). https://doi.org/10.1007/s12652-022-04026-1
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DOI: https://doi.org/10.1007/s12652-022-04026-1