AMEDDE-Net: A Deeply Supervised Multi-Scale Attention Network for Breast Cancer Lesion Segmentation in Digital Mammography
Received: 2 March 2026 | Revised: 8 April 2026 | Accepted: 21 April 2026 | Online: 6 June 2026
Corresponding author: Mauridhi Hery Purnomo
Abstract
This study proposes the Attention, Multi-scale, Enhanced, Dilated, Dual-head, Ensemble Network (AMEDDE-Net), a deeply supervised segmentation framework that integrates multi-scale feature extraction, enhanced attention mechanisms, dilated convolutions, dual-head prediction, and adaptive ensemble fusion to improve lesion-focused learning and boundary delineation. The model was evaluated using a publicly available Contrast-Enhanced Spectral Mammography (CESM) dataset and five-fold cross-validation. Extensive hyperparameter tuning, ablation studies, and comparisons with state-of-the-art convolutional and transformer-based models were conducted. The experimental results indicated that AMEDDE-Net achieved superior performance, with 97.25% accuracy, a Dice coefficient of 65.10%, an F1-score of 65.10%, and a recall of 71.83%, outperforming conventional U-Net variants and the transformer-based TransU-Net. Ablation analyses confirmed the importance of multi-scale attention and deep supervision, whereas qualitative results demonstrated improved boundary accuracy and reduced False Negatives. These findings highlight the potential of AMEDDE-Net as a reliable tool for automated breast cancer screening and clinical decision support.
Keywords:
cancer segmentation, digital mammography, deep supervision, multi-scale attention, deep learning, medical image analysisReferences
F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, "Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA: A Cancer Journal for Clinicians, vol. 68, no. 6, pp. 394–424, 2018.
C. D. Lehman et al., "National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium," Radiology, vol. 283, no. 1, pp. 49–58, Apr. 2017.
M. Woo et al., "Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography," PLOS Digital Health, vol. 4, no. 4, Apr. 2025, Art. no. e0000811.
Y. J. Suh, J. Jung, and B.-J. Cho, "Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning," Journal of Personalized Medicine, vol. 10, no. 4, Nov. 2020, Art. no. 211.
H. Zunair and A. Ben Hamza, "Sharp U-Net: Depthwise convolutional network for biomedical image segmentation," Computers in Biology and Medicine, vol. 136, Sept. 2021, Art. no. 104699.
S. Li, M. Dong, G. Du, and X. Mu, "Attention Dense-U-Net for Automatic Breast Mass Segmentation in Digital Mammogram," IEEE Access, vol. 7, pp. 59037–59047, May 2019.
A. J. Bewersdorf, E. L. Bewersdorf, G. M. Fundaro, and G. Fundaro, "Mammographically Occult Breast Cancer in a Patient With Dense Breast Tissue," Cureus, vol. 17, no. 1, 2025, Art. no. e77789.
M. R. Islam et al., "Enhancing breast cancer segmentation and classification: An Ensemble Deep Convolutional Neural Network and U-net approach on ultrasound images," Machine Learning with Applications, vol. 16, June 2024, Art. no. 100555.
S. Anari, S. Sadeghi, G. Sheikhi, R. Ranjbarzadeh, and M. Bendechache, "Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models," Scientific Reports, vol. 15, no. 1, Jan. 2025, Art. no. 1027.
H. Li, D. Chen, W. H. Nailon, M. E. Davies, and D. I. Laurenson, "Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography," IEEE Transactions on Medical Imaging, vol. 41, no. 1, pp. 3–13, Jan. 2022.
S. M. Shaaban, M. Nawaz, Y. Said, and M. Barr, "An Efficient Breast Cancer Segmentation System based on Deep Learning Techniques," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12415–12422, Dec. 2023.
P. Kumar et al., "An AI-Based Method for Automated Breast Cancer Detection and Localization in Mammogram and Ultrasound Images," Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 27266–27272, Oct. 2025.
J. Chen et al., "TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers," Medical Image Analysis, vol. 97, Oct. 2024, Art. no. 103280.
J. Schlemper et al., "Attention gated networks: Learning to leverage salient regions in medical images," Medical Image Analysis, vol. 53, pp. 197–207, Apr. 2019.
S. Ahmad, E. S. Neal Joshua, N. T. Rao, R. M. Ghoniem, B. M. Taye, and S. Bharany, "A multi stage deep learning model for accurate segmentation and classification of breast lesions in mammography," Scientific Reports, vol. 15, no. 1, Oct. 2025, Art. no. 37103.
L. Wang, "Self-supervised learning and transformer-based technologies in breast cancer imaging," Frontiers in Radiology, vol. 5, Nov. 2025.
J. Chen, L. Chen, S. Wang, and P. Chen, "A novel multi-scale adversarial networks for precise segmentation of x-ray breast mass," IEEE access, vol. 8, pp. 103772–103781, Jun. 2020.
V. K. Singh et al., "Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network," Expert Systems with Applications, vol. 139, Jan. 2020, Art. no. 112855.
Y. Gao, J. Lin, Y. Zhou, and R. Lin, "The application of traditional machine learning and deep learning techniques in mammography: a review," Frontiers in Oncology, vol. 13, Aug. 2023.
P. Buelens, S. Willems, L. Vandewinckele, W. Crijns, F. Maes, and C. G. Weltens, "Clinical evaluation of a deep learning model for segmentation of target volumes in breast cancer radiotherapy," Radiotherapy and Oncology, vol. 171, pp. 84–90, June 2022.
D. Dave et al., "Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review," PeerJ Computer Science, vol. 11, Feb. 2025, Art. no. e2476.
A. Jalalian, S. B. T. Mashohor, H. R. Mahmud, M. I. B. Saripan, A. R. B. Ramli, and B. Karasfi, "Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review," Clinical Imaging, vol. 37, no. 3, pp. 420–426, May 2013.
M. Sreevani and R. Latha, "An Advanced Ensemble of Deep Learning Models for Breast Cancer Segmentation and Classification with Two-Tier Optimization Algorithms," Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 27024–27029, Oct. 2025.
Categorized Digital Database for Low energy and Subtracted Contrast Enhanced Spectral Mammography images, The Cancer Imaging Archive.
R. Khaled et al., "Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research," Scientific Data, vol. 9, no. 1, Mar. 2022, Art. no. 122.
A. Baccouche, B. Garcia-Zapirain, C. Castillo Olea, and A. S. Elmaghraby, "Connected-UNets: a deep learning architecture for breast mass segmentation," npj Breast Cancer, vol. 7, no. 1, Dec. 2021, Art. no. 151.
J. H. Park, J. H. Lim, S. Kim, and J. Heo, "A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis," In Vivo, vol. 38, no. 6, pp. 2864–2872, Nov. 2024.
K. Zhao, J. Prokop, J. Montalt-Tordera, and S. Mohammadi, "Panoptic Segmentation of Mammograms with Text-to-Image Diffusion Model," in 4th MICCAI Workshop, DGM4MICCAI 2024, Marrakesh, Morocco, Oct. 2024.
S. Acosta-Jiménez, M. M. Mendoza-Mendoza, C. E. Galván-Tejada, J. M. Celaya-Padilla, J. I. Galván-Tejada, and M. A. Soto-Murillo, "Explainable Deep Learning for Breast Lesion Classification in Digital and Contrast-Enhanced Mammography," Diagnostics, vol. 15, no. 24, Dec. 2025, Art. no. 3143.
Downloads
How to Cite
License
Copyright (c) 2026 Dyah Titisari, Andrew Prasetyo, Eko Mulyanto Yuniarno, I Ketut Eddy Purnama, Mauridhi Hery Purnomo
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
