Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning

Yu Wei; Qiming Liu; Yiying Wan; Ying Wang; Guozhi Li; Ge Zhang

doi:10.1117/12.3009220

16 October 2023 Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning

Yu Wei, Qiming Liu, Yiying Wan, Ying Wang, Guozhi Li, Ge Zhang

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032E (2023) https://doi.org/10.1117/12.3009220
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

The new incidence rate of rectal cancer accounts for 12.2% of all cancers, which seriously affects the health of the people. In addition, the early clinical diagnosis of T staging of rectal cancer will strongly support the formulation of effective treatment plans. However, it is difficult for clinicians to accurately determine the T stage of rectal cancer before operation because the site of rectal tumor invasion is not obvious. Therefore, this paper first proposes a bilinear feature fusion mechanism, which effectively avoids the problem of information loss in the process of convolution neural network training of rectal cancer MRI images; Secondly, the new weighted loss function designed can solve the problem of multi-example imbalance; Finally, the experiment proves that the constructed intelligent prediction strategy for T-stage of rectal cancer has a good accuracy, which provides a good auxiliary result for the clinical treatment of rectal cancer.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yu Wei, Qiming Liu, Yiying Wan, Ying Wang, Guozhi Li, and Ge Zhang "Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032E (16 October 2023); https://doi.org/10.1117/12.3009220

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