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.
To improve the deficiency of the control system of finish cooling temperature (FCT), a new model developed from a combination of a multilayer perception neural network as the self-learning system and traditional mathematical model were brought forward to predict the plate FCT. The relationship between the self-learning factor of heat transfer coefficient and its influencing parameters such as plate thickness, start cooling temperature, was investigated. Simulative calculation indicates that the deficiency of FCT control system is overcome completely, the accuracy of FCT is obviously improved and the difference between the calculated and target FCT is controlled between −15 ℃ and 15 ℃.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.