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سی و دومین کنفرانس ملی و دهمین کنفرانس بین المللی مهندسی زیست پزشکی ایران
Fibroglandular Tissue Classification in Breast MRI: A Comparative Study of Automated Decision Strategies
نویسندگان :
Meysam Khalaj
1
Arvin Arian
2
Ala Torabi
3
Nasrin Ahmadinejad
4
Masoumeh Gity
5
Seyedeh Nooshin Miratashi Yazdi
6
Mohammad Pooya Afshari
7
Melika Sadeghi Tabrizi
8
Hamid Soltanian-Zadeh
9
1- University of Tehran
2- Tehran University of Medical Sciences
3- Tehran University of Medical Sciences
4- Tehran University of Medical Sciences
5- Tehran University of Medical Sciences
6- Tehran University of Medical Sciences
7- University of Tehran
8- University of Tehran
9- University of Tehran
کلمات کلیدی :
Fibroglandular Tissue Classification،Breast MRI،BI-RADS Assessment،Deep Learning،Shannon Entropy
چکیده :
Fibroglandular tissue (FGT) assessment in breast magnetic resonance imaging (MRI) is clinically important for breast cancer risk evaluation and is standardized in the Breast Imaging Reporting and Data System (BI-RADS) lexicon. While automated approaches have largely focused on segmentation, classification-based methods remain underexplored. Previous automated FGT classification studies have generally analyzed both breasts together, overlooking BI-RADS recommendations for side-specific evaluation and alternative strategies such as probability averaging or uncertainty-based rules. This study evaluates three assessment strategies: the conventional BI-RADS Maximum Rule, a novel Probability Averaging Rule to integrate bilateral information, and a novel Lower-Uncertainty Rule based on Shannon entropy to prioritize more confident predictions. These strategies were assessed using three diverse deep learning architectures, MobileNetV2, ResNeXt-26, and a hybrid ViT-ResNet, selected to analyze performance across models with different architectures and feature extraction mechanisms. The dataset comprised 654 pre-contrast 3D axial T1-weighted fat-saturated breast MRI scans, with each breast evaluated independently. Across ten independent runs, ViT-ResNet with Probability Averaging Rule achieved the highest test accuracy (0.85), F1 score (0.84), and Cohen’s kappa (0.78), while violin plot analysis showed that the Lower-Uncertainty Rule produced the lowest predictive entropy. Both proposed strategies consistently outperformed the conventional rule. The curated, expert-annotated dataset is publicly released to support reproducible research in this domain.
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