Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer

IEEE Journal of Translational Engineering in Health and Medicine

Yun Yang¹  Jing Guo²  Yaowei Wang³  Minghao Yu³  Yongchun Duan³  Kelong Wang³  Dehai Zhang³  Lin Wu⁴  Zhenhui Li⁴ 

[1] Key Laboratory in Software Engineering of Yunnan Province, School of Software, Yunnan University, Kunming, China;School of Information Science and Engineering, Yunnan University, Kunming, China;School of Software, Yunnan University, Kunming, China;Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China;

关键词: Pervasive computing;    neoadjuvant chemoradiotherapy;    internet of things;    pathological images;    rectal cancer;   

DOI  :  10.1109/JTEHM.2022.3156851

来源: DOAJ

【 摘 要 】

Background: At present, radical total mesorectal excision after neoadjuvant chemoradiotherapy is crucial for locally advanced rectal cancer. Therefore, the use of histopathological images analysis technology to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer is of great significance for the subsequent treatment of patients. Methods: In this study, we propose a new pathological images analysis method based on multi-instance learning to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. Specifically, we proposed a gated attention normalization mechanism based on the multilayer perceptron, which accelerates the convergence of stochastic gradient descent optimization and can speed up the training process. We also proposed a bilinear attention multi-scale feature fusion mechanism, which organically fuses the global features of the larger receptive fields and the detailed features of the smaller receptive fields and alleviates the problem of pathological images context information loss caused by block sampling. At the same time, we also designed a weighted loss function to alleviate the problem of imbalance between cancerous instances and normal instances. Results: We evaluated our method on a locally advanced rectal cancer dataset containing 150 whole slide images. In addition, to verify our method’s generalization performance, we also tested on two publicly available datasets, Camelyon16 and MSKCC. The results show that the AUC values of our method on the Camelyon16 and MSKCC datasets reach 0.9337 and 0.9091, respectively. Conclusion: Our method has outstanding performance and advantages in predicting the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. Clinical and Translational Impact Statement—This study aims to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer to assist clinicians quickly diagnose and formulate personalized treatment plans for patients.

【 授权许可】

Unknown