Open AccessMulti-View Attention-based Late Fusion (MVALF) CADx system for breast cancer using deep learning
Author(s) -
Hina Iftikhar,
Basit Raza,
Basit Raza,
Hasan Nasir Khan
Publication year - 2020
Publication title -
machine graphics and vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.105
H-Index - 13
eISSN - 2720-250X
pISSN - 1230-0535
DOI - 10.22630/mgv.2020.29.1.4
Subject(s) - mammography , artificial intelligence , weighting , breast cancer , computer science , feature (linguistics) , medicine , pattern recognition (psychology) , cancer , radiology , linguistics , philosophy
Breast cancer is a leading cause of death among women. Early detection can significantly reduce the mortality rate among women and improve their prognosis. Mammography is the first line procedure for early diagnosis. In the early era, conventional Computer-Aided Diagnosis (CADx) systems for breast lesion diagnosis were based on just single view information. The last decade evidence the use of two views mammogram: Medio-Lateral Oblique (MLO) and Cranio-Caudal (CC) view for the CADx systems. Most recent studies show the effectiveness of four views of mammogram to train CADx system with feature fusion strategy for classification task. In this paper, we proposed an end-to-end Multi-View Attention-based Late Fusion (MVALF) CADx system that fused the obtained predictions of four view models, which is trained for each view separately. These separate models have different predictive ability for each class. The appropriate fusion of multi-view models can achieve better diagnosis performance. So, it is necessary to assign the proper weights to the multi-view classification models. To resolve this issue, attention-based weighting mechanism is adopted to assign the proper weights to trained models for fusion strategy. The proposed methodology is used for the classification of mammogram into normal, mass, calcification, malignant masses and benign masses. The publicly available datasets CBIS-DDSM and mini-MIAS are used for the experimentation. The results show that our proposed system achieved 0.996 AUC for normal vs. abnormal, 0.922 for mass vs. calcification and 0.896 for malignant vs. benign masses. Superior results are seen for the classification of malignant vs benign masses with our proposed approach, which is higher than the results using single view, two views and four views early fusion-based systems. The overall results of each level show the potential of multi-view late fusion with transfer learning in the diagnosis of breast cancer.