Multi-view information fusion in mammograms: A comprehensive overview

Jouirou, Amira; Baâzaoui, Abir; Barhoumi, Walid

doi:10.1016/j.inffus.2019.05.001

Cited by 34 publications

(12 citation statements)

References 74 publications

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“…In the training phase, for getting the information from multiple views, early fusion is applied. Early fusion is the technique of merging multiple feature vectors into single feature vector [36]. The four-views' features' fusion based CADx system comprises of two strategies.…”

Section: Proposed Four-view Features Fusion Based Cadx Systemmentioning

confidence: 99%

Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

et al. 2019

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Breast cancer is the second most common cause of cancer-related deaths among women. Early detection leads to better prognosis and saves lives. The 5-year survival rate of breast cancer is 99% if it is located only in breast. Conventional computer-aided diagnosis (CADx) systems for breast cancer use the single view information of mammograms to assist the radiologists. More recent work has focused on more than one views. Existing multi-view based CADx systems normally employ only two views namely Cranio-Caudal (CC) and Medio-Lateral-Oblique (MLO). The information fusion of the two views proved the effectiveness of the system for mammogram classification which cannot be achieved by single view information. However, combining the information of four views of mammograms increases the performance of classification. In this study, we propose Multi-View Feature Fusion (MVFF) based CADx system using feature fusion technique of four views for classification of mammogram. The complete CADx tool contains three stages, the first stage have the ability to classify mammogram into abnormal or normal, second stage is about classification of mass or calcification and in the final stage classification of malignant or benign classification is performed. Convolutional Neural Network (CNN) based feature extraction models operate on each view separately. These extracted features were fused into one final layer for ultimate prediction. Our proposed system is trained on four views of mammograms, after data augmentation. We performed our experiments on publicly available datasets such as CBIS-DDSM (Curated Breast Imaging Subset of DDSM) and mini-MIAS database of mammograms. In comparison with literature the MVFF based system is performed better than a single view-based system for mammogram classification. We have achieved area under ROC curve (AUC) of 0.932 for mass and calcification and 0.84 for malignant and benign, which is higher than all single-view based systems. The value of AUC for normal and abnormal classification is 0.93.

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Section: Proposed Four-view Features Fusion Based Cadx Systemmentioning

confidence: 99%

Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

et al. 2019

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“…For the CBIS-DDSM dataset, the mass samples from the two mammogram views: Craniocaudal (CC) and the mediolateral-oblique (MLO) views were extracted and used in the four experiments of the CAD system [30,80].…”

Section: Cbis-ddsm Datasetmentioning

confidence: 99%

A framework for breast cancer classification using Multi-DCNNs

Ragab

Attallah

Sharkas

et al. 2021

Computers in Biology and Medicine

125

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“…Lotter et al [44] and Shen et al [18] fused local CNN patch features, whereas the former extracted them with a sliding window approach, and the latter extracted only CNN features from salient regions obtained with a global image classifier. Another common approach is to utilize multiple views for localization and classification of lesions and full images, as summarized by Jouirou et al [50]. While Shachor et al [37] dynamically combined classical features from local patches from MLO and CC view for calcification classification, Kooi et al [27] fused CNN features from ROIs across views for malignant mass detection.…”

Section: A Related Workmentioning

confidence: 99%

Multi-task fusion for improving mammography screening data classification

Wimmer,

Sluiter,

Major

et al. 2021

Preprint

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Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., the classification of lesions or the prediction of a mammogram's pathology status. To obtain a comprehensive view of a patient, models which were all trained for the same task(s) are subsequently ensembled or combined. In this work, we propose a pipeline approach, where we first train a set of individual, taskspecific models and subsequently investigate the fusion thereof, which is in contrast to the standard model ensembling strategy. We fuse model predictions and high-level features from deep learning models with hybrid patient models to build stronger predictors on patient level. To this end, we propose a multi-branch deep learning model which efficiently fuses features across different tasks and mammograms to obtain a comprehensive patient-level prediction. We train and evaluate our full pipeline on public mammography data, i.e., DDSM and its curated version CBIS-DDSM, and report an AUC score of 0.962 for predicting the presence of any lesion and 0.791 for predicting the presence of malignant lesions on patient level. Overall, our fusion approaches improve AUC scores significantly by up to 0.04 compared to standard model ensembling. Moreover, by providing not only global patient-level predictions but also task-specific model results that are related to radiological features, our pipeline aims to closely support the reading workflow of radiologists.

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Multi-view information fusion in mammograms: A comprehensive overview

Cited by 34 publications

References 74 publications

Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

A framework for breast cancer classification using Multi-DCNNs

Multi-task fusion for improving mammography screening data classification

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