TY - GEN
T1 - Boundary-aware Semi-supervised Deep Learning for Breast Ultrasound Computer-Aided Diagnosis
AU - Zhang, Erlei
AU - Seiler, Stephen
AU - Chen, Mingli
AU - Lu, Weiguo
AU - Gu, Xuejun
PY - 2019/7
Y1 - 2019/7
N2 - Breast ultrasound (US) is an effective imaging modality for breast cancer diagnosis. US computer-aided diagnosis (CAD) systems have been developed for decades and have employed either conventional handcrafted features or modern automatic deep-learned features, the former relying on clinical experience and the latter demanding large datasets. In this paper, we developed a novel BASDL method that integrates clinical-approved breast lesion boundary characteristics (features) into a semi-supervised deep learning (SDL) to achieve accurate diagnosis with a small training dataset. Original breast US images are converted to boundary-oriented feature maps (BFMs) using a distance-transformation coupled with a Gaussian filter. Then, the converted BFMs are used as the input of SDL network, which is characterized as lesion classification guided unsupervised image reconstruction based on stacked convolutional auto-encode (SCAE). We compared the performance of BASDL with conventional SCAE method and SDL method that used the original images as inputs, as well as SCAE method that used BFMs as inputs. Experimental results on two breast US datasets show that BASDL ranked the best among the four networks, with classification accuracy around 92.00±2.38%, which indicated that BASDL could be promising for effective breast US lesion CAD using small datasets.
AB - Breast ultrasound (US) is an effective imaging modality for breast cancer diagnosis. US computer-aided diagnosis (CAD) systems have been developed for decades and have employed either conventional handcrafted features or modern automatic deep-learned features, the former relying on clinical experience and the latter demanding large datasets. In this paper, we developed a novel BASDL method that integrates clinical-approved breast lesion boundary characteristics (features) into a semi-supervised deep learning (SDL) to achieve accurate diagnosis with a small training dataset. Original breast US images are converted to boundary-oriented feature maps (BFMs) using a distance-transformation coupled with a Gaussian filter. Then, the converted BFMs are used as the input of SDL network, which is characterized as lesion classification guided unsupervised image reconstruction based on stacked convolutional auto-encode (SCAE). We compared the performance of BASDL with conventional SCAE method and SDL method that used the original images as inputs, as well as SCAE method that used BFMs as inputs. Experimental results on two breast US datasets show that BASDL ranked the best among the four networks, with classification accuracy around 92.00±2.38%, which indicated that BASDL could be promising for effective breast US lesion CAD using small datasets.
UR - http://www.scopus.com/inward/record.url?scp=85077887967&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077887967&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2019.8856539
DO - 10.1109/EMBC.2019.8856539
M3 - Conference contribution
C2 - 31946050
AN - SCOPUS:85077887967
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 947
EP - 950
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -