TY - GEN
T1 - Dual Fusion Mass Detector for Mammogram Mass Detection
AU - Liu, Shuo
AU - Lai, Zhihui
AU - Kong, Heng
AU - Shen, Linlin
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Dual Fusion Mass Detector
KW - dataset
KW - feature fusion
KW - mammogram mass detection
UR - http://www.scopus.com/inward/record.url?scp=85137919526&partnerID=8YFLogxK
U2 - 10.1109/CBMS55023.2022.00033
DO - 10.1109/CBMS55023.2022.00033
M3 - Conference contribution
AN - SCOPUS:85137919526
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 149
EP - 154
BT - Proceedings - 2022 IEEE 35th International Symposium on Computer-Based Medical Systems, CBMS 2022
A2 - Shen, Linlin
A2 - Gonzalez, Alejandro Rodriguez
A2 - Santosh, KC
A2 - Lai, Zhihui
A2 - Sicilia, Rosa
A2 - Almeida, Joao Rafael
A2 - Kane, Bridget
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022
Y2 - 21 July 2022 through 23 July 2022
ER -