Two‐step evidential fusion approach for accurate breast region segmentation in mammograms

Lajili, Rihab; Kalti, Karim; Touil, Asma; Solaiman, Basel; Amara, Najoua Essoukri Ben

doi:10.1049/iet-ipr.2018.5325

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Few other studies incorporated evidential theory, specifically for breast cancer tumor classification [20] and breast region segmentation [73], they only utilized a single mammogram dataset, which may not be a good representative of different data distributions and mammogram-based assessment tasks. In contrast, we trained and validated our approach, MV-DEFEAT, on various multi view publicly available mammogram datasets, which exhibit variations in intensity, image resolution, and image format.…”

Section: Discussionmentioning

confidence: 99%

A Multi-View Deep Evidential Learning Approach for Mammogram Density Classification

Gudhe,

Mazen,

Sund

et al. 2024

IEEE Access

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Artificial intelligence algorithms, specifically deep learning, can assist radiologists by automating mammogram density assessment. However, trust in such algorithms must be established before they are widely adopted in clinical settings. In this study, we present an evidential deep learning approach called MV-DEFEAT, incorporating the strength of Dempster Shafer evidential theory and subjective logic, for the mammogram density classification task. The framework combines evidence from multiple mammograms' views to mimic a radiologist decision making process. In this study, we utilized four open-source datasets, namely VinDr-Mammo, DDSM, CMMD, and VTB, to mitigate inherent biases and provide a diverse representation of the data. Our experimental findings demonstrate MV-DEFEAT's superior performance in terms of weighted macro-average area under the receiver operating curves (AUCs) compared to the state-ofthe-art multi-view deep learning model, referred to as MVDL. MV-DEFEAT yields a relative improvement of 12.57%, 14.51%, 19.9%, and 22.53%, on the VTB, VinDr-Mammo, CMMD, and DDSM datasets, respectively, for the mammogram density classification task. Additionally, for BIRADS classification and the classification of mammograms as benign or malignant, MV-DEFEAT exhibits substantial enhancements compared to MVDL, with a relative improvement of 31.46% and 50.78% on the DDSM and VinDr-Mammo datasets, respectively. These results underscore the efficacy of our approach. Through meticulous curation of diverse datasets and comprehensive comparative analyses, we ensure the robustness and reliability of our findings, thereby enhancing trust to adopt MV-DEFEAT framework for various mammogram assessment tasks in clinical settings.

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Section: Discussionmentioning

confidence: 99%

A Multi-View Deep Evidential Learning Approach for Mammogram Density Classification

Gudhe,

Mazen,

Sund

et al. 2024

IEEE Access

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“…Moreover, the standard Dirichlet distribution provides a simple and intuitive way to measure uncertainty in model predictions, which makes it easier to interpret and evaluate the results. While other studies also incorporated evidential theory, specifically Dempster-Shafer evidential theory and combination rule, for breast cancer tumor classification 62 and breast region segmentation 63 , they only utilized a single mammogram dataset, which may not be representative for different distributions and assessment tasks. In contrast, we trained and validated our approach, MV-DEFEAT, on multi-view publicly available datasets, which also exhibit variations in intensity, image resolution, and image 9/16 format.…”

Section: Discussionmentioning

confidence: 99%

An evidential deep learning framework for assessment of mammograms

Gudhe,

Mazen,

Sund

et al. 2023

Preprint

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In this study, we present an evidential deep learning framework called MV-DEFEAT, incorporating the strength of Dempster-Shafer evidential theory and subjective logic, for various mammogram assessment tasks, including mammogram density assessment, BIRADS scoring, and mammogram finding as normal/benign/malignant. The framework combines evidence from multiple mammogram’s views to mimic a radiologist’s decision-making process. We conducted experiments on two open-source digital mammogram datasets, VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography and the Digital Database for Screening Mammography (DDSM), which include 5,000 and 1,975 mammogram examinations, respectively. Our results showed that MV-DEFEAT achieves weighted macro-average area under the receiver operating characteristic curves (AUCs) of 98.92 ± 0.007 and 86.53 ± 0.051 for mammogram density assessment on the VinDr-Mammo and DDSM datasets, respectively, which are considerably better than a state-of-the-art multi-view deep learning method with AUCs of 86.38 ± 0.258 and 70.62 ± 0.261. In addition, MV-DEFEAT demonstrated strong generalization for mammogram density assessment on two independent and unseen datasets, the Chinese Mammography Database (CMMD) and Virtual Tissue Biobank (VTB). Furthermore, we demonstrated the efficient feature representation of the MV-DEFEAT that could be transferred to a breast cancer stage classification task using the unseen Kuopio Osteoporosis Risk Factor and Prevention study (OSTPRE) dataset. Our results enhance trust in using deep learning algorithms to assess mammograms in clinical settings.

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“…In [141], Lajili et al proposed a two-step evidential fusion approach for breast region segmentation. The first evidential segmentation results were obtained by a gray-scale-based K-means clustering method, resulting in k classes.…”

Section: Multimodal Evidence Fusionmentioning

confidence: 99%