Breast cancer has become a killer of women's health nowadays.In order to exploit the potential representational capabilities of the models more comprehensively,we propose a multi-model fusion strategy.Specifically...Breast cancer has become a killer of women's health nowadays.In order to exploit the potential representational capabilities of the models more comprehensively,we propose a multi-model fusion strategy.Specifically,we combine two differently structured deep learning models,ResNet101 and Swin Transformer(SwinT),with the addition of the Convolutional Block Attention Module(CBAM)attention mechanism,which makes full use of SwinT's global context information modeling ability and ResNet101's local feature extraction ability,and additionally the cross entropy loss function is replaced by the focus loss function to solve the problem of unbalanced allocation of breast cancer data sets.The multi-classification recognition accuracies of the proposed fusion model under 40X,100X,200X and 400X BreakHis datasets are 97.50%,96.60%,96.30 and 96.10%,respectively.Compared with a single SwinT model and ResNet 101 model,the fusion model has higher accuracy and better generalization ability,which provides a more effective method for screening,diagnosis and pathological classification of female breast cancer.展开更多
Breast cancer has become a common tumor worldwide which seriously endangers people＇s health. Earlydiagnosis and treatment are particularly urgent in order to reduce the onset risk, mortality, and prolongthe five-year...Breast cancer has become a common tumor worldwide which seriously endangers people＇s health. Earlydiagnosis and treatment are particularly urgent in order to reduce the onset risk, mortality, and prolongthe five-year survival rate. Therefore, we need a kind of diagnosis and treatment technology with highspecificity, sensitivity and selectivity. In recent years, because of its unique properties in biologicalapplications, fluorescence imaging has become an attractive research subject. Fluorescence imagingoffers innovative ideas of targetable recognition of breast cancer cells, breast cancer imaging in vivoanimal models, anticancer drugs delivery for guiding the mammary surgery via a noninvasive way withhigh sensitively and specifically. In this review, we summarized the recent advances of fluorescent probesfor breast cancer imaging, which were classified according to different biomarkers the probes recognized.Moreover, we discussed the strengths, built-in problems as well as the challenges about the fluorescentprobe as a unique potential method for the better application in breast cancer diagnosis and treatment.~ 2018 Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.展开更多
基金By the National Natural Science Foundation of China(NSFC)(No.61772358),the National Key R&D Program Funded Project(No.2021YFE0105500),and the Jiangsu University‘Blue Project’.
文摘Breast cancer has become a killer of women's health nowadays.In order to exploit the potential representational capabilities of the models more comprehensively,we propose a multi-model fusion strategy.Specifically,we combine two differently structured deep learning models,ResNet101 and Swin Transformer(SwinT),with the addition of the Convolutional Block Attention Module(CBAM)attention mechanism,which makes full use of SwinT's global context information modeling ability and ResNet101's local feature extraction ability,and additionally the cross entropy loss function is replaced by the focus loss function to solve the problem of unbalanced allocation of breast cancer data sets.The multi-classification recognition accuracies of the proposed fusion model under 40X,100X,200X and 400X BreakHis datasets are 97.50%,96.60%,96.30 and 96.10%,respectively.Compared with a single SwinT model and ResNet 101 model,the fusion model has higher accuracy and better generalization ability,which provides a more effective method for screening,diagnosis and pathological classification of female breast cancer.
基金financial supports from the National Natural Science Foundation of China(Nos.31370391,81772812,21422606,21402191)Dalian Cultivation Fund for Distinguished Young Scholars(Nos.2014J11JH130 and 2015J12JH205)The Foundation of Dalian Science Department(No.2015E12SF149)
文摘Breast cancer has become a common tumor worldwide which seriously endangers people＇s health. Earlydiagnosis and treatment are particularly urgent in order to reduce the onset risk, mortality, and prolongthe five-year survival rate. Therefore, we need a kind of diagnosis and treatment technology with highspecificity, sensitivity and selectivity. In recent years, because of its unique properties in biologicalapplications, fluorescence imaging has become an attractive research subject. Fluorescence imagingoffers innovative ideas of targetable recognition of breast cancer cells, breast cancer imaging in vivoanimal models, anticancer drugs delivery for guiding the mammary surgery via a noninvasive way withhigh sensitively and specifically. In this review, we summarized the recent advances of fluorescent probesfor breast cancer imaging, which were classified according to different biomarkers the probes recognized.Moreover, we discussed the strengths, built-in problems as well as the challenges about the fluorescentprobe as a unique potential method for the better application in breast cancer diagnosis and treatment.~ 2018 Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.
