Dual Fusion Mass Detector for Mammogram Mass Detection

Shuo Liu, Zhihui Lai, Heng Kong, Linlin Shen

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


Mammogram mass detection is a difficult task due to the mass character of the tiny area, fuzzy boundary, and occlusion. To address these problems, this paper proposes a novel detection network for mammogram mass detection. Firstly, we propose a novel feature fusion structure and Small Target Attention Module (STAM) to improve the model's ability to detect small masses. Secondly, Results-oriented Loss (ROL) is adopted to obtain better model performance. Finally, Incremental Positive Selection (IPS) is used to divide positive and negative anchors. The scarcity of breast mammogram images for training aggravates the difficulty of mass detection. Thus, we open our collected dataset, which contains 1456 mammogram images from 400 patients. Since the model includes a double feature fusion structure, the proposed network is named Dual Fusion Mass Detector (DFMD). Experiment results show that DFMD is robust to various variations on scale, blurry and occlusion.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE 35th International Symposium on Computer-Based Medical Systems, CBMS 2022
EditorsLinlin Shen, Alejandro Rodriguez Gonzalez, KC Santosh, Zhihui Lai, Rosa Sicilia, Joao Rafael Almeida, Bridget Kane
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665467704
Publication statusPublished - 2022
Externally publishedYes
Event35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022 - Shenzhen, China
Duration: 21 Jul 202223 Jul 2022

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
ISSN (Print)1063-7125


Conference35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022


  • dataset
  • Dual Fusion Mass Detector
  • feature fusion
  • mammogram mass detection

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications


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