TY - GEN
T1 - Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound
AU - Wang, Na
AU - Bian, Cheng
AU - Wang, Yi
AU - Xu, Min
AU - Qin, Chenchen
AU - Yang, Xin
AU - Wang, Tianfu
AU - Li, Anhua
AU - Shen, Dinggang
AU - Ni, Dong
N1 - Funding Information:
Acknowledgments. This work was supported in part by the National Natural Science Foundation of China under Grant 61571304, Grant 61701312, and in part by the Shenzhen Peacock Plan under Grant KQTD2016053112051497.
Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Automated breast ultrasound (ABUS) is a new and promising tool for diagnosing breast cancer. However, reviewing ABUS images is extremely time-consuming and oversight errors could happen. We propose a novel 3D convolutional network for automatic cancer detection in ABUS. Our contribution is twofold. First, we propose a threshold loss function to provide voxel-level adaptive threshold for discriminating cancer and non-cancer, thus achieving high sensitivity with low FPs. Second, we propose a densely deep supervision (DDS) mechanism to improve the sensitivity significantly by utilizing multi-scale discriminative features of all layers. Both class-balanced cross entropy loss and overlap loss are employed to enhance DDS performance. The efficacy of the proposed network is validated on a dataset of 196 patients with 661 cancer regions. The 4-fold cross-validation experiments show our network obtains a sensitivity of 93% with 2.2 FPs per ABUS volume. Our proposed novel network can provide an accurate and automatic cancer detection tool for breast cancer screening by maintaining high sensitivity with low FPs.
AB - Automated breast ultrasound (ABUS) is a new and promising tool for diagnosing breast cancer. However, reviewing ABUS images is extremely time-consuming and oversight errors could happen. We propose a novel 3D convolutional network for automatic cancer detection in ABUS. Our contribution is twofold. First, we propose a threshold loss function to provide voxel-level adaptive threshold for discriminating cancer and non-cancer, thus achieving high sensitivity with low FPs. Second, we propose a densely deep supervision (DDS) mechanism to improve the sensitivity significantly by utilizing multi-scale discriminative features of all layers. Both class-balanced cross entropy loss and overlap loss are employed to enhance DDS performance. The efficacy of the proposed network is validated on a dataset of 196 patients with 661 cancer regions. The 4-fold cross-validation experiments show our network obtains a sensitivity of 93% with 2.2 FPs per ABUS volume. Our proposed novel network can provide an accurate and automatic cancer detection tool for breast cancer screening by maintaining high sensitivity with low FPs.
UR - http://www.scopus.com/inward/record.url?scp=85053855553&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00937-3_73
DO - 10.1007/978-3-030-00937-3_73
M3 - Conference contribution
AN - SCOPUS:85053855553
SN - 9783030009366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 641
EP - 648
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Frangi, Alejandro F.
A2 - Fichtinger, Gabor
A2 - Schnabel, Julia A.
A2 - Alberola-López, Carlos
A2 - Davatzikos, Christos
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -
