Estimation of corresponding locations in ipsilateral mammograms: a comparison of different methods

Wilms, Matthias; Krüger, Julia; Marx, Mirko; Ehrhardt, Jan; Bischof, Arpad; Handels, Heinz

doi:10.1117/12.2081862

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…The assumption is widely adopted in previous researches on mammograms since many radiologists routinely use the nipple to estimate the correspondence objects on different views of the same breast. 15,17 Thus, if the D-AN in the CC view is close to the D-AN in the MLO view, then the two AD candidates will be considered a potential corresponding AD pairs (and vice versa). In this study, we set the criterion for "close to" based on the statistics of the manual AD annotation on training dataset.…”

Section: Two-view Fusion Approachmentioning

confidence: 99%

Multi-view based computer-aided model with anatomical position prior for architectural distortion detection in digital breast tomosynthesis

Ma¹,

He²,

Li³

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

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Architectural distortion (AD) is one of the important breast abnormal signs in digital breast tomosynthesis (DBT). It is hard to be detected due to its subtle appearance and similar intensity with surrounding tissue. To assist radiologists to detect ADs, a single-view based computer-aided detection model in DBT was developed by us previously. In this study, considering the fact that radiologists always use information from craniocaudal (CC) and mediolateral oblique (MLO) views of DBT simultaneously for better diagnosis of each breast in clinic, we further develop a multi-view based AD detection model in DBT that combines the information from the two views. In this model, AD candidates in each view are detected by our previous AD detection model. Anatomical position priors of AD candidates in the two views are considered through establishing a 3D anatomical coordinate system. A multi-view based classifier is trained to fuse information from the two views and distinguish the true AD candidates. A dataset of 196 CC-MLO DBT pairs were collected with IRB approval, 101 of them contained ADs and the remaining were negative pairs. Ten-fold cross-validation showed that after involving our proposed multi-view method, the sensitivities of AD detection at 1, 2, 3 and 4 false positive predictions per DBT pairs improved from 0.66, 0.73, 0.77 and 0.79 to 0.69, 0.77, 0.78, and 0.83, respectively. The results showed that the multi-view based model achieved better detection performance than single-view based model. This model has potential to assist radiologists in detection of ADs in DBT.

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Section: Two-view Fusion Approachmentioning

confidence: 99%

Multi-view based computer-aided model with anatomical position prior for architectural distortion detection in digital breast tomosynthesis

Ma¹,

He²,

Li³

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

View full text Add to dashboard Cite

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Towards Robust Dual-View Transformation via Densifying Sparse Supervision for Mammography Lesion Matching

Xian

Wang

Cheng

et al. 2021

Lecture Notes in Computer Science

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TwoViewDensityNet: Two-View Mammographic Breast Density Classification Based on Deep Convolutional Neural Network

et al. 2022

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Dense breast tissue is a significant factor that increases the risk of breast cancer. Current mammographic density classification approaches are unable to provide enough classification accuracy. However, it remains a difficult problem to classify breast density. This paper proposes TwoViewDensityNet, an end-to-end deep learning-based method for mammographic breast density classification. The craniocaudal (CC) and mediolateral oblique (MLO) views of screening mammography provide two different views of each breast. As the two views are complementary, and dual-view-based methods have proven efficient, we use two views for breast classification. The loss function plays a key role in training a deep model; we employ the focal loss function because it focuses on learning hard cases. The method was thoroughly evaluated on two public datasets using 5-fold cross-validation, and it achieved an overall performance (F-score of 98.63%, AUC of 99.51%, accuracy of 95.83%) on DDSM and (F-score of 97.14%, AUC of 97.44%, accuracy of 96%) on the INbreast. The comparison shows that the TwoViewDensityNet outperforms the state-of-the-art methods for classifying breast density into BI-RADS class. It aids healthcare providers in providing patients with more accurate information and will help improve the diagnostic accuracy and reliability of mammographic breast density evaluation in clinical care.

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Estimation of corresponding locations in ipsilateral mammograms: a comparison of different methods

Cited by 3 publications

References 6 publications

Multi-view based computer-aided model with anatomical position prior for architectural distortion detection in digital breast tomosynthesis

Multi-view based computer-aided model with anatomical position prior for architectural distortion detection in digital breast tomosynthesis

Towards Robust Dual-View Transformation via Densifying Sparse Supervision for Mammography Lesion Matching

TwoViewDensityNet: Two-View Mammographic Breast Density Classification Based on Deep Convolutional Neural Network

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