Skip to main navigation Skip to search Skip to main content

The combinations of loss functions and schemes for mammographic classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The analysis of mammographic images effectively is a burning question recently. So the research of binary classification whether a whole mammographic image has masses or not is proposed in this paper. The two main issues are the class imbalance of INbreast database and the effect of classification. To address these, focal loss, center loss and three kinds of schemes are proposed. Focal loss is put forward to address class imbalance by multiplying a modulating factor. Center loss concerns about enhancement of discriminative power by handling inter-class features, that is similar to the first two schemes. Moreover, the third scheme is used to solve the class imbalance with L-{1} norm. The experimental analyses demonstrate the effectiveness of the combination of diverse loss functions and schemes. The convergence rate increases to varying degrees.

Original languageEnglish
Title of host publication2018 IEEE 4th International Conference on Computer and Communications, ICCC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1594-1599
Number of pages6
ISBN (Electronic)9781538683392
DOIs
StatePublished - Dec 2018
Event4th IEEE International Conference on Computer and Communications, ICCC 2018 - Chengdu, China
Duration: 7 Dec 201810 Dec 2018

Publication series

Name2018 IEEE 4th International Conference on Computer and Communications, ICCC 2018

Conference

Conference4th IEEE International Conference on Computer and Communications, ICCC 2018
Country/TerritoryChina
CityChengdu
Period7/12/1810/12/18

Keywords

  • Center loss
  • Class imbalance
  • Focal loss
  • Mammograph

Fingerprint

Dive into the research topics of 'The combinations of loss functions and schemes for mammographic classification'. Together they form a unique fingerprint.

Cite this