Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound

Na Wang, Cheng Bian, Yi Wang, Min Xu, Chenchen Qin, Xin Yang, Tianfu Wang, Anhua Li, Dinggang Shen, Dong Ni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsAlejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos
PublisherSpringer Verlag
Pages641-648
Number of pages8
ISBN (Print)9783030009366
DOIs
Publication statusPublished - 2018 Jan 1
Externally publishedYes
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 2018 Sep 162018 Sep 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11073 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period18/9/1618/9/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound'. Together they form a unique fingerprint.

  • Cite this

    Wang, N., Bian, C., Wang, Y., Xu, M., Qin, C., Yang, X., Wang, T., Li, A., Shen, D., & Ni, D. (2018). Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound. In A. F. Frangi, G. Fichtinger, J. A. Schnabel, C. Alberola-López, & C. Davatzikos (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings (pp. 641-648). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11073 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00937-3_73