Cover Image

Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sharifian, Peyman, Hong, Xiaotong, Karimian, Alireza, Amini, Mehdi, Arabi, Hossein
Format:	Preprint
Published:	2025
Subjects:	Image and Video Processing Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Transformer-Based Explainable Deep Learning for Breast Cancer Detection in Mammography: The MammoFormer Framework
by: Peter, Ojonugwa Oluwafemi Ejiga, et al.
Published: (2025)

MamT$^4$: Multi-view Attention Networks for Mammography Cancer Classification
by: Ibragimov, Alisher, et al.
Published: (2024)

Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
by: Thrun, Solveig, et al.
Published: (2025)

A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography
by: Rofena, Aurora, et al.
Published: (2023)

ManiNeg: Manifestation-guided Multimodal Pretraining for Mammography Classification
by: Li, Xujun, et al.
Published: (2024)

Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods
by: Abbasniya, Mohammad Reza, et al.
Published: (2022)

Cross-Attention Multimodal Fusion for Breast Cancer Diagnosis: Integrating Mammography and Clinical Data with Explainability
by: Nantogmah, Muhaisin Tiyumba, et al.
Published: (2025)

Enhanced Tuberculosis Bacilli Detection using Attention-Residual U-Net and Ensemble Classification
by: K, Greeshma, et al.
Published: (2025)

Attention-Map Augmentation for Hypercomplex Breast Cancer Classification
by: Lopez, Eleonora, et al.
Published: (2023)

Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classification
by: Panambur, Adarsh Bhandary, et al.
Published: (2024)

Breast Tumor Classification Using EfficientNet Deep Learning Model
by: Behzadpour, Majid, et al.
Published: (2024)

A study on Deep Convolutional Neural Networks, Transfer Learning and Ensemble Model for Breast Cancer Detection
by: Ahad, Md Taimur, et al.
Published: (2024)

Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study
by: Eskandari, Sania, et al.
Published: (2024)

DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation
by: Akyüz, Uğurcan, et al.
Published: (2025)

Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification
by: Hussain, Sadam, et al.
Published: (2024)

External Validation of Deep Learning Models for BI-RADS Breast Density Prediction from Ultrasound Images
by: Chen, Yuxuan, et al.
Published: (2026)

Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography
by: Ghosh, Shantanu, et al.
Published: (2024)

Deep Learning Predicts Mammographic Breast Density in Clinical Breast Ultrasound Images
by: Bunnell, Arianna, et al.
Published: (2024)

Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer
by: Pal, Surochita, et al.
Published: (2025)

MAMBO: High-Resolution Generative Approach for Mammography Images
by: Škipina, Milica, et al.
Published: (2025)

Deep Learning for Lung Disease Classification Using Transfer Learning and a Customized CNN Architecture with Attention
by: Liu, Xiaoyi, et al.
Published: (2024)

Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography
by: Chen, Xuxin, et al.
Published: (2024)

Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation
by: Musah, Toufiq, et al.
Published: (2025)

Deep Learning-Based Breast Cancer Detection in Mammography: A Multi-Center Validation Study in Thai Population
by: Chamveha, Isarun, et al.
Published: (2025)

An Optimized Ensemble Deep Learning Model For Brain Tumor Classification
by: Talukder, Md. Alamin, et al.
Published: (2023)

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings
by: Fadugba, Jeremiah, et al.
Published: (2025)

Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images
by: Ahmadi, Mohsen, et al.
Published: (2023)

Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning
by: Hannan, Abdul, et al.
Published: (2025)

Breast Cancer Image Classification Method Based on Deep Transfer Learning
by: Wang, Weimin, et al.
Published: (2024)

SKIPNet: Spatial Attention Skip Connections for Enhanced Brain Tumor Classification
by: Mendiratta, Khush, et al.
Published: (2024)

Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI
by: Ahmed, Maryam, et al.
Published: (2024)

Breast Cancer Classification in Deep Ultraviolet Fluorescence Images Using a Patch-Level Vision Transformer Framework
by: Afshin, Pouya, et al.
Published: (2025)

Multi-Sensor Attention Networks for Automated Subsurface Delamination Detection in Concrete Bridge Decks
by: Moayedikia, Alireza, et al.
Published: (2025)

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
by: Luo, Luyang, et al.
Published: (2023)

Efficient Feature Extraction Using Light-Weight CNN Attention-Based Deep Learning Architectures for Ultrasound Fetal Plane Classification
by: Sivasubramanian, Arrun, et al.
Published: (2024)

Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification
by: Tschuchnig, Maximilian, et al.
Published: (2025)

Kernel Density Steering: Inference-Time Scaling via Mode Seeking for Image Restoration
by: Hu, Yuyang, et al.
Published: (2025)

Adaptive Fusion of Radiomics and Deep Features for Lung Adenocarcinoma Subtype Recognition
by: Zhou, Jing, et al.
Published: (2023)

Uncertainty-Aware Deep Video Compression with Ensembles
by: Ma, Wufei, et al.
Published: (2024)

ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic Features
by: Wei, Xin, et al.
Published: (2024)