@misc{10481/65582,
year = {2020},
month = {11},
url = {http://hdl.handle.net/10481/65582},
abstract = {Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal
therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and
although it has high accuracy (~88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial
intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence
based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240
DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and
rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were
chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08±1.22%, a specificity
of 93.58±1.49 and an accuracy of 93.83±0.96. The proposed method gives superior performance than eight state-of-theart
approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve
diagnostic accuracy.},
organization = {British Heart Foundation Accelerator Award, UK},
organization = {Royal Society International Exchanges Cost Share Award, UK 	
RP202G0230},
organization = {Hope Foundation for Cancer Research, UK 	
RM60G0680},
organization = {Medical Research Council Confidence in Concept Award, UK 	
MC_PC_17171},
organization = {MINECO/FEDER, Spain/Europe 	
RTI2018-098913-B100
A-TIC-080-UGR18},
publisher = {Springer Nature},
keywords = {Ductal carcinoma in situ},
keywords = {Thermal images},
keywords = {Deep learning},
keywords = {Convolutional neural networks},
keywords = {Breast thermography},
keywords = {Exponential linear unit},
keywords = {Rank-based weighted pooling},
keywords = {Data augmentation},
keywords = {Color jittering},
keywords = {Visual question answering},
title = {Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling},
doi = {10.1007/s40747-020-00218-4},
author = {Zhang, Yu-Dong and Gorriz Sáez, Juan Manuel},
}