A review of explainable AI techniques and their evaluation in mammography for breast cancer screening

Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699 · see citing works

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works

Generative adversarial networks

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384 · see citing works

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989 · see citing works

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072 · see citing works

Advances in Neural Information Processing Systems 19

(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,037 · see citing works

Learning Deep Features for Discriminative Localization

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428 · see citing works

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works

Denoising Diffusion Probabilistic Models

Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712 · see citing works

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01228-7 · Cited by 6,909 · see citing works

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

(2016) · doi:10.1145/2939672 · Cited by 6,563 · see citing works

Mammographic Density and the Risk and Detection of Breast Cancer

Boyd, N. F., Guo, H., Martin, L. J., Sun, L., Stone, J., Fishell, E., Jong, R. A., Hislop, G., Chiarelli, A. M., Minkin, S., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa062790 · Cited by 2,482 · see citing works

The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI

Shin, D. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102551 · Cited by 1,548 · see citing works

Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis

Feng, Y., Spezia, M., Huang, S., Yuan, C., Zeng, Z., Zhang, L., Ji, X., Liu, W., Huang, B., Luo, W., et al. (2018) · Genes & Diseases · doi:10.1016/j.gendis.2018.05.001 · Cited by 1,537 · see citing works

INbreast

Moreira, I. C., Amaral, I., Domingues, I., Cardoso, A. N. L., Cardoso, M. J., & Cardoso, J. S. (2011) · Academic Radiology · doi:10.1016/j.acra.2011.09.014 · Cited by 1,193 · see citing works

Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging

Najjar, R. (2023) · Diagnostics · doi:10.3390/diagnostics13172760 · Cited by 961 · see citing works

A curated mammography data set for use in computer-aided detection and diagnosis research

Lee, R. S., Gimenez, F., Hoogi, A., Miyake, K. K., Gorovoy, M., & Rubin, D. L. (2017) · Scientific Data · doi:10.1038/sdata.2017.177 · Cited by 859 · see citing works

Survey of Explainable AI Techniques in Healthcare

Chaddad, A., Peng, J., Xu, J., & Bouridane, A. (2023) · Sensors · doi:10.3390/s23020634 · Cited by 591 · see citing works

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Zheng, H., Dubitzky, W., Hu, X., Hao, J., Berrar, D., Cho, K.-H., Wang, Y., & David, G. (2021) · doi:10.1109/bibm52615.2021 · Cited by 378 · see citing works

Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives

Geras, K. J., Mann, R. M., & Moy, L. P. (2019) · Radiology · doi:10.1148/radiol.2019182627 · Cited by 298 · see citing works

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir, S., Dickson, D. M., & Akram, M. U. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106668 · Cited by 283 · see citing works

Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams

Shen, Y., Shamout, F. E., Oliver, J. R., Witowski, J. S., Kannan, K., Park, J., Wu, N., Huddleston, C., Wolfson, S., Millet, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26023-2 · Cited by 278 · see citing works

Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches

Borys, K., Schmitt, Y. A., Nauta, M., Seifert, C., Krämer, N., Friedrich, C. M., & Nensa, F. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110786 · Cited by 233 · see citing works

From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies

Srinivasu, P. N., Sandhya, N., Jhaveri, R. H., & Raut, R. (2022) · Mobile Information Systems · doi:10.1155/2022/8167821 · Cited by 187 · see citing works

Mammographic Image Analysis Society (MIAS) database v1.21

John Suckling, A., Parker, J., Dance, D. A. B., Astley, S., Hutt, I., Boggis, C. R. M., Ricketts, I. W., Stamatakis, E. A., Cerneaz, N., Kok, S. Y., et al. (2015) · Apollo (University of Cambridge) · doi:10.17863/cam.105113 · Cited by 176 · see citing works

A Comprehensive Review on Breast Cancer Detection, Classification and Segmentation Using Deep Learning

Abhisheka, B., Biswas, S. K., & Purkayastha, B. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09968-z · Cited by 175 · see citing works

Breast cancer detection in mammograms using convolutional neural network

Charan, S., Khan, M. J., & Khurshid, K. (2018) · International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) · doi:10.1109/icomet.2018.8346384 · Cited by 168 · see citing works

TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages

Maqsood, S., Damaševičius, R., & Maskeliūnas, R. (2022) · Applied Sciences · doi:10.3390/app12073273 · Cited by 165 · see citing works

Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review

Ghasemi, A., Hashtarkhani, S., Schwartz, D. L., & Shaban‐Nejad, A. (2024) · Cancer Innovation · doi:10.1002/cai2.136 · Cited by 161 · see citing works

A Yolo-Based Model for Breast Cancer Detection in Mammograms

Prinzi, F., Insalaco, M., Orlando, A. A. M., Gaglio, S., & Vitabile, S. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10189-6 · Cited by 151 · see citing works

A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging

Gulum, M. A., Trombley, C. M., & Kantardzic, M. M. (2021) · Applied Sciences · doi:10.3390/app11104573 · Cited by 144 · see citing works

Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning

Suh, Y. J., Jung, J., & Cho, B. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10040211 · Cited by 135 · see citing works

2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)

(2018) · doi:10.1109/icomet42805.2018 · Cited by 107 · see citing works

Diagnostic Accuracy of Digital Mammography in the Detection of Breast Cancer

Zeeshan, M., Salam, B., Khalid, Q. S. B., Alam, S., & Sayani, R. (2018) · Cureus · doi:10.7759/cureus.2448 · Cited by 96 · see citing works

Attention guided grad-CAM : an improved explainable artificial intelligence model for infrared breast cancer detection

Raghavan, K., Sivaselvan, B., & Kamakoti, V. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17776-7 · Cited by 93 · see citing works

A novel breast cancer detection architecture based on a CNN-CBR system for mammogram classification

Bouzar-Benlabiod, L., Harrar, K., Yamoun, L., Khodja, M. Y., & Akhloufi, M. A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107133 · Cited by 89 · see citing works

A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: Limited use of explainable AI?

Groen, A. M., Kraan, R., Amirkhan, S. F., Daams, J. G., & Maas, M. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110592 · Cited by 89 · see citing works

Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD

Mayo, R. C., Kent, D., Sen, L. Q. C., Kapoor, M. M., Leung, J. W. T., & Watanabe, A. T. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-0168-6 · Cited by 87 · see citing works

XAI-RACapsNet: Relevance aware capsule network-based breast cancer detection using mammography images via explainability O-net ROI segmentation

Alhussen, A., Haq, M. A., Khan, A. A., Mahendran, R. K., & Kadry, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125461 · Cited by 85 · see citing works

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

Gurmessa, D. K., & Jimma, W. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100954 · Cited by 74 · see citing works

Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection

Prodan, M., Paraschiv, E. A., & Stanciu, A. (2023) · Applied Sciences · doi:10.3390/app13074272 · Cited by 69 · see citing works

Transparency of artificial intelligence/machine learning-enabled medical devices

Shick, A. A., Webber, C. M., Kiarashi, N., Weinberg, J. P., Deoras, A., Petrick, N., Saha, A., & Diamond, M. C. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00992-8 · Cited by 67 · see citing works

Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis

Abdullakutty, F. C., Akbari, Y., Al-Maadeed, S. A., Bouridane, A., Talaat, I. M., & Hamoudi, R. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1450103 · Cited by 65 · see citing works

A new xAI framework with feature explainability for tumors decision-making in Ultrasound data: comparing with Grad-CAM

Song, D., Yao, J., Jiang, Y., Shi, S., Cui, C., Wang, L., Wang, L., Wu, H., Tian, H., Ye, X., et al. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107527 · Cited by 60 · see citing works

High-Resolution Mammogram Synthesis using Progressive Generative Adversarial NetworksPreprint

Korkinof, D., Rijken, T. M., O’Neill, M., Yearsley, J. E., Harvey, H., & Glocker, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.03401 · Cited by 57 · see citing works

The impact of AI suggestions on radiologists' decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination

Rezazade Mehrizi, M. H., Mol, F., Peter, M., Ranschaert, E., Dos Santos, D. P., Shahidi, R., Fatehi, M., & Dratsch, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-36435-3 · Cited by 54 · see citing works

AI Trust: Can Explainable AI Enhance Warranted Trust?

de Brito Duarte, R., Correia, F., Arriaga, P., & Paiva, A. (2023) · Human Behavior and Emerging Technologies · doi:10.1155/2023/4637678 · Cited by 52 · see citing works

Quantitative evaluation of Saliency-Based Explainable artificial intelligence (XAI) methods in Deep Learning-Based mammogram analysis

Cerekci, E. A., Alis, D. C., Denizoğlu, N., Camurdan, O., Şeker, M. E., Özer, C., Hansu, M. Y., Tanyel, T., Öksüz, İ., & Karaarslan, E. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111356 · Cited by 52 · see citing works

Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images

Al-Tam, R. M., Al-Hejri, A. M., Alshamrani, S. S., Al–antari, M. A., & Narangale, S. M. (2024) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2024.08.007 · Cited by 48 · see citing works

An Causal XAI Diagnostic Model for Breast Cancer Based on Mammography Reports

Chen, D., Zhao, H., He, J., Pan, Q., & Zhao, W. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669648 · Cited by 38 · see citing works

DeepMiCa: Automatic Segmentation and Classification of Breast Microcalcifications from Mammograms

Gerbasi, A., Clementi, G., Corsi, F., Albasini, S., Malovini, A. L., Quaglini, S., & Bellazzi, R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107483 · Cited by 37 · see citing works

An efficient computational algorithm for Hausdorff distance based on points-ruling-out and systematic random sampling

Ryu, J., & Kamata, S. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107857 · Cited by 31 · see citing works

An Explainable AI System for Medical Image Segmentation With Preserved Local Resolution: Mammogram Tumor Segmentation

Farrag, A., Gad, G., Fadlullah, Z. M., Fouda, M. M., & Alsabaan, M. (2023) · IEEE Access · doi:10.1109/access.2023.3330465 · Cited by 29 · see citing works

Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches

Akatsuka, J., Yamamoto, Y., Sekine, T., Numata, Y., Morikawa, H., Tsutsumi, K., Yanagi, M., Endo, Y., Takeda, H., Hayashi, T., et al. (2019) · Biomolecules · doi:10.3390/biom9110673 · Cited by 27 · see citing works

Saliency of breast lesions in breast cancer detection using artificial intelligence

Pertuz, S. D., Ortega, D., Suarez, É., Cancino, W., Africano, G., Rinta-Kiikka, I. C., Arponen, O., Paris, S., & Lozano, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-46921-3 · Cited by 25 · see citing works

X-Brain: Explainable recognition of brain tumors using robust deep attention CNN

Tonmoy, M. R., Shams, M. A., Adnan, M. A., Mridha, M. F., Safran, M., Alfarhood, S., & Che, D. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106988 · Cited by 23 · see citing works

A convolutional deep learning model for improving mammographic breast-microcalcification diagnosis

Kang, D., Gweon, H. M., Eun, N. L., Youk, J. H., Kim, J., & Son, E. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-03516-0 · Cited by 22 · see citing works

2024 3rd International Conference for Innovation in Technology (INOCON)

(2024) · doi:10.1109/inocon60754.2024 · Cited by 22 · see citing works

Comparison of Explainable Artificial Intelligence Model and Radiologist Review Performances to Detect Breast Cancer in 752 Patients

Öztekin, P. S., KATAR, O., Omma, T., Erel, S., Tokur, O., Avcı, D., Aydoğan, M., Yıldırım, Ö., Avcı, E., & Acharya, U. R. (2024) · Journal of Ultrasound in Medicine · doi:10.1002/jum.16535 · Cited by 17 · see citing works

Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover

Lee, J., & Nishikawa, R. M. (2024) · Breast Cancer Research · doi:10.1186/s13058-024-01777-x · Cited by 14 · see citing works

Artificial Intelligence Applications and Innovations

Maglogiannis, I. G., Iliadis, L., MacIntyre, J., & Domí­nguez, M. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-34107-6 · Cited by 12 · see citing works

Advancing Breast Cancer Detection with Convolutional Neural Networks: A Comparative Analysis of MIAS and DDSM Datasets

Alhsnony, F. H., & Sellami, L. (2024) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip62566.2024.10638886 · Cited by 12 · see citing works

Explainable Artificial Intelligence to Detect Breast Cancer: A Qualitative Case-Based Visual Interpretability Approach

Rodriguez-Sampaio, M., Rincón, M., Valladares‐Rodríguez, S., & Bachiller, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06242-1_55 · Cited by 12 · see citing works

Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms

Anyfantis, D., Koutras, A., Apostolopoulos, G., & Christoyianni, I. (2023) · Signals · doi:10.3390/signals4020022 · Cited by 11 · see citing works

Two-stage mammography classification model using explainable-AI for ROI detection

Dahl, F. A., Brautaset, O., Holden, M., Eikvil, L., Larsen, M., & Hofvind, S. S. H. (2023) · Nordic Machine Intelligence · doi:10.5617/nmi.10459 · Cited by 11 · see citing works

Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

Chakravarthy, S. R. S., Bharanidharan, N., Khan, S. B., Venkatesan, V. K., Ramakrishna, M. T., Almusharraf, A. I., & Aurungzeb, K. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.052531 · Cited by 10 · see citing works

Impact of Mammography Screening Frequency on Breast Cancer Mortality Rates

Abeelh, E. A., & AbuAbeileh, Z. (2023) · Cureus · doi:10.7759/cureus.49066 · Cited by 10 · see citing works

Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature

Bhalla, D., Rangarajan, K., Chandra, T., Banerjee, S., & Arora, C. (2023) · Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging · doi:10.1055/s-0043-1775737 · Cited by 9 · see citing works

MetaXAI: Metahuman-assisted audio and visual explainability framework for Internet of Medical Things

Kök, İ. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107034 · Cited by 8 · see citing works

Improved GAN for image resolution enhancement using ViT for breast cancer detection

Rautela, K., Kumar, D., & Kumar, V. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22998 · Cited by 8 · see citing works

Breast Cancer Prediction by Ensembling Machine Learning Algorithms and Explainable AI

Sobhana, M., Palaketi, A. K., & Nalabothu, R. (2024) · International Conference for Innovation in Technology (INOCON) · doi:10.1109/inocon60754.2024.10512277 · Cited by 8 · see citing works

Ductal carcinoma in situ (DCIS)

Ashraf, A. (2023) · Radiopaedia.org · doi:10.53347/rid-164661 · Cited by 3 · see citing works

2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP)

(2024) · doi:10.1109/atsip62566.2024 · Cited by 3 · see citing works

eXplainable Artificial Intelligence (XAI): A Systematic Review for Unveiling the Black Box Models and Their Relevance to Biomedical Imaging and Sensing

Hettikankanamage, N. D., Shafiabady, N., Chatteur, F., Wu, M., Din, F. U., & Zhou, J. J. (2025) · Sensors · doi:10.3390/s25216649 · Cited by 63 · see citing works

Explainable multimodal fusion for breast carcinoma diagnosis: A systematic review, open problems, and future directions

Hassan, M. M., Tahsin, A., Alam, M. G. R., Alzamil, D., Garg, S., Uddin, M. Z., Choudhury, N., & Fortino, G. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109152 · Cited by 18 · see citing works

Explainable AI-enabled hybrid deep learning architecture for breast cancer detection

Zou, Y., & Miao, P. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1658741 · Cited by 15 · see citing works

An Explainable and Lightweight CNN Framework for Robust Potato Leaf Disease Classification Using Grad‐ CAM Visualization

Hoque, M. J., & Islam, S. (2025) · Applied AI Letters · doi:10.1002/ail2.70011 · Cited by 14 · see citing works

Electromagnetic field confinement-driven multi-resonator THz biosensing platform with Au–Ag–graphene hybrid coatings and angle-invariant machine learning prediction for point-of-care breast cancer diagnostics

Vijayarajan, S. M., Deepika, G., Devi, K. M. S., & Alagarsamy, M. (2025) · Microsystem Technologies · doi:10.1007/s00542-025-05997-9 · Cited by 14 · see citing works

Evaluating Explainability: A Framework for Systematic Assessment of Explainable AI Features in Medical Imaging

Lago, M. A., Zamzmi, G., Eich, B., & Delfino, J. G. (2026) · Bioengineering · doi:10.3390/bioengineering13010111 · Cited by 12 · see citing works

Overcoming the Black Box Challenge: Building Trust in Artificial Intelligence Algorithms in Oncology

Alum, E. U., Egwu, C. K., Manjula, V. S., Ekpang, P. O., Ekpang, J. E., Echegu, D. A., Alum, B. N., & Uti, D. E. (2026) · Technology in Cancer Research & Treatment · doi:10.1177/15330338261434649 · Cited by 12 · see citing works

HyFormer-Net: A synergistic CNN-Transformer with interpretable multi-scale fusion for breast lesion segmentation and classification in ultrasound images

Rahman, M. A. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100413 · Cited by 9 · see citing works

Recent advancements in machine learning and deep learning for early detection of breast cancer: A comprehensive review

Smrity, T. A., Muntaqim, M. Z., & Kaf, H. M. (2026) · Innovative Practice in Breast Health · doi:10.1016/j.ibreh.2026.100050 · Cited by 9 · see citing works

Mam-Incept-Net: a novel inception model for precise interpretation of mammography images

Gursel, A. T., & Kaya, Y. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3149 · Cited by 8 · see citing works

Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice

Lastrucci, A., Iosca, N., Wandael, Y., Barra, A., Ricci, R., Cucchiari, J. N., Forini, N., Lepri, G., & Giansanti, D. (2025) · Diagnostics · doi:10.3390/diagnostics15172197 · Cited by 7 · see citing works

Explainable artificial intelligence in endodontics: Concepts, clinical applications, and future directions

Turky, M., Samaranayake, L. P., Osathanon, T., & Dummer, P. M. H. (2026) · Journal of Dentistry · doi:10.1016/j.jdent.2026.106733 · Cited by 6 · see citing works

Explainable Deep Learning for Breast Lesion Classification in Digital and Contrast-Enhanced Mammography

Acosta-Jiménez, S., Mendoza-Mendoza, M. M., Galvan-Tejada, C. E., Celaya-Padilla, J. M., Galván-Tejada, J. I., & Soto-Murillo, M. A. (2025) · Diagnostics · doi:10.3390/diagnostics15243143 · Cited by 5 · see citing works

Use of Artificial Intelligence Methods for Improved Diagnosis of Urinary Tract Infections and Urinary Stone Disease

Pantilimonescu, T. F., Dămian, C., Radu, V. D., Hogea, M., Costachescu, O. A., Onofrei, P., Toma, B., Zelinschi, D. O., Roca, I. C., Ursu, R. G., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14144942 · Cited by 5 · see citing works

A Novel Hybrid CNN-ViT-Based Bi-Directional Cross-Guidance Fusion-Driven Breast Cancer Detection Model

Sait, A. R. W., & Alkhurayyif, Y. (2026) · Life · doi:10.3390/life16030474 · Cited by 2 · see citing works

Evaluating cognitive biases in AI-assisted mammography interpretation: a simulation reader study of explainable AI across radiologist experience levels

Pesapane, F., Latronico, A., Abbate, F., Penco, S., Rotili, A., Dominelli, V., Nicosia, L., Monzani, D., Grasso, R., Pravettoni, G., et al. (2026) · European Radiology · doi:10.1007/s00330-026-12666-6 · Cited by 2 · see citing works

A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification

Barki, C., Chouchen, M., Sediri, A., Ceylan, H. İ., Rahmouni, H. B., Muntean, R. I., El-Seedi, H. R., Bragazzi, N. L., & Dergaa, I. (2026) · Frontiers in Systems Biology · doi:10.3389/fsysb.2026.1892418 · Cited by 1 · see citing works

Breast Cancer Classification Using CNN–DenseNet Hybrid Model with LIME-based Explainability

Verma, V., Chawla, S. K., Mittal, T., & Gupta, K. (2025) · Asia Pacific Conference on Innovation in Technology (APCIT) · doi:10.1109/apcit65661.2025.11410944 · Cited by 1 · see citing works

Explainable Machine Learning Models SHAP-based for Feature Importance Affecting Stunting Prevalence

Pasaribu, A. A., Sahamony, N. F., Notodiputro, K. A., & Sartono, B. (2026) · ComTech Computer Mathematics and Engineering Applications · doi:10.21512/comtech.v17i1.13732 · Cited by 1 · see citing works

Diagnostic Accuracy of AI-assisted Mammography in Breast Cancer Detection: A Retrospective Study from a South Asian Population

Sami, M. U., Saeed, M., & Nazeem, S. (2026) · Asian Pacific Journal of Cancer Nursing · doi:10.31557/apjcn.2561.20260531 · Cited by 1 · see citing works

Explainable vision transformer framework for breast lesion classification with Grad-CAM support

Kumar, A., Khan, M., Mittal, G., Husain, I., Shukla, S., Ali, S. A., Sayal, A., Jha, J., & Ziar, R. A. (2026) · Cancer Imaging · doi:10.1186/s40644-026-01097-7 · Cited by 1 · see citing works

Edge-refined cross-task multi-level dense fusion for accurate breast ultrasound segmentation and classification

Arısoy, M. V. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131793

Explainable Transform Learning for Interpretable Medical Image Analysis

Santosh Kumar, B., Nagarajan, A., Sumathi, S., Prabakaran, S., Eric Clapten, J., & Murugan, V. (2025) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca66325.2025.11280392

Digitale Brust-Tomosynthese und Befundung mit künstlicher Intelligenz

Weigel, S., Wunderlich, P., Sommer, A., Czwoydzinski, J., Baltzer, P., Dewald, C. L. A., Preibsch, H., Sauer, S. T., Siegmann-Luz, K., Wenkel, E., et al. (2026) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-2796-5225

From Black Box to Glass Box: A Survey of Explainable AI in Mammographic Screening

Hashim, S. M., & Kutucu, H. (2026) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis...1766498

Enabling Uncertainty Measurement in Multi-subregion Tumor Segmentation: BraTS 2025 Pediatrics

Namdar, K., Mirjalili, S., Kim, S., Deniffel, D., Brunt, K. R., Celi, L. A., Cusimano, M. D., & Tyrrell, P. N. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-16365-3_36

Two-level breast tumor classification with multi-view imaging and pathology-aware large language models: A fuzzy KNN and ensemble learning framework

Sharafi, Y., Teshnehlab, M., & Sadighi, N. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115210

Patient and Public Perceptions of Artificial Intelligence in Breast Imaging and Clinical Decision-Making: An Exploratory Cross-Sectional Survey Study

Hussein, A., Rizk, M., Mokbel, K., & Carmichael, A. R. (2026) · Diagnostics · doi:10.3390/diagnostics16091376

A COMPREHENSIVE SURVEY ON AI-DRIVEN MAMMOGRAPHY-BASED BREAST CANCER DETECTION

Shinde, P., Kamble, S., & Srivaramangai, R. (2026) · doi:10.34218/ijaimed_04_01_003

Embedded Shift Evolution in Explainable Deep Learning Systems

Ziweritin, S., Gunasekaran, A., Iram, S., & Chen, M. (2026) · Studies in big data · doi:10.1007/978-3-032-21009-8_12

Interpreting classification models: Understanding and applying classification models with practical examples

Erdağı, N. Ö., Gunduz, Y. E., & Öztürk, Ş. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44163-9.00012-1

Explainable AI in breast cancer ultrasound imaging: current developments and challenges

Hameed, M., & Sim, K. S. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1846763

Stakeholder experience with artificial intelligence in healthcare: a bibliometric study of satisfaction, trust, acceptance, and patient engagement

Li, X. J., Jing, C., Gong, Z., Zhang, J., & Zhang, S. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1842497

AI-Driven Microcalcification Detection in Digital Mammography for Early Breast Cancer Diagnosis: A Scoping Review, Challenges, Limitations, and Future Perspectives

Domínguez, H. D. J. O., Lozoya, R. S. L., Sánchez, V. G. C., Villegas, O. O. V., Sossa, H., & Ramírez, E. S. (2026) · Mathematics · doi:10.3390/math14132367

Comparative Evaluation of ResNet50 and DenseNet121 with SE-Block for Breast Cancer Classification

Darmawan, M. M. M., & Sunyoto, A. (2026) · JOINTECS (Journal of Information Technology and Computer Science) · doi:10.31328/jointecs.v11i1.7676

The Evolving Landscape of Deep Learning in Breast Cancer Imaging: A Bibliometric Study of Segmentation, Detection, and Diagnostic Research From 2006 to 2025

Zeng, S., Wang, L., Zhang, Y., Deng, Y., Shen, X., & Li, Y. (2026) · Cancer Control · doi:10.1177/10732748261469229

Breast cancer detection and classification via a robust deep learning approach

Makram, M., Aziz, A. S., Saeid, M. M., & Mohamed, M. T. (2026) · Scientific Reports · doi:10.1038/s41598-026-62255-2

Artificial Intelligence in MSK Radiology: Current Applications, LLMs, Agentic AI, and Future Directions

Chapala, S., Gibson, J. G., Chinniah, P. K., Kiyawat, V., & Botchu, R. (2026) · Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging · doi:10.1055/s-0046-1825800

Breast tumor segmentation and classification using kolmogorov-arnold hyperbolic transformer convolutional neural network with black‑winged kite algorithm from mammogram images

Kanna, R. R., Hariprasad, N., Selvam, P., & Sujithra, L. R. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110961

Cross-View Attention Routing in Capsule Networks for Structured Interpretable Multi-View Mammogram Breast Cancer Classification

Li, X., Dong, Q., Liu, W., Wu, M., & Xu, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-35393-1_7

Artificial Intelligence for Breast Cancer Detection in Mammography: A Review

Kamel, A., Chiba, Z., Moumen, S. E., & Ennaqui, S. (2026) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd69640.2026.11637310

Deep Learning in Multimodal Breast Cancer Imaging: From Image Reconstruction and Segmentation to Diagnosis and Treatment Response Prediction

Bartusik‐Aebisher, D., Czech, S., Szpara, J., Paul, A., Xavierselvan, M., & Aebisher, D. (2026) · Applied Sciences · doi:10.3390/app16178771

Explainable Artificial Intelligence for Breast Cancer Classification Using Deep Neural Networks

Mehra, G., Manchanda, M., Kumar, P., Shuaib, M., & Kumar, A. (2026) · International Conference on Green Technologies and Sustainable Solutions (GTSS) · doi:10.1109/gtss68743.2026.11679796

Breast Lesion Classification: Leakage-free Patch-based Transfer Learning with Cross-dataset Evaluation on CBIS-DDSM, MIAS, and INbreast

Kag, H., Kumar, D., & Diwan, A. (2026) · Jordanian Journal of Computers and Information Technology · doi:10.5455/jjcit.71-1783079555

Digitale Brust-Tomosynthese und Befundung mit künstlicher Intelligenz

Weigel, S., Wunderlich, P., Sommer, A., Czwoydzinski, J., Baltzer, P., Dewald, C. L. A., Preibsch, H., Sauer, S. T., Siegmann-Luz, K., Wenkel, E., et al. (2026) · Senologie - Zeitschrift für Mammadiagnostik und -therapie · doi:10.1055/a-2911-2000

Artificial intelligence in breast cancer research: a systematic review and bibliometric analysis of emerging trends and future directions

Mohamed, Y. B., ZainEldin, H. Y., Eladl, S. G., Sayed, H. A., Shehata, M., Badawy, M., & Elhosseini, M. A. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1800218

Bridging scales in brain health: Explainable artificial intelligence and multimodal imaging for integrated, regenerative neuro‑care

Reyed, R. M., & Prabhakar, P. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27722-1.00012-2

Breast Cancer Detection and Prognosis using Mammographic and Genomic Data: A Comprehensive Review of Radiogenomics, Multimodal Deep Learning, and Explainable Artificial Intelligence

Sharma, G. K., & Kumar, A. (2026) · International Journal of Advanced Engineering Research and Science · doi:10.22161/ijaers.131.6

Novel Screening and Early Detection Strategies in Breast Cancer

El-Ella, D. M. A., Samreen, B., Shanbhag, N. M., & Raza, A. (2026) · Springer eBooks · doi:10.1007/978-981-92-5842-0_3

Artificial intelligence in breast cancer therapy

Singh, M., Kumar, S., Nehra, B., & Anjum, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-49074-3.00023-6

Cultivating Healthcare Robotics Innovation in Emerging Economies

Rani, Z., Raj, A., Sharma, D., Rajput, M., Naaz, Z., & Sharma, V. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1483-7.ch002

HCT-Mammo: A Compact Hybrid CNN-Transformer for Benign–Malignant Mammogram Classification with Uncertainty and Cross-Dataset Evaluation

Borsha, M. K. N., Rahman, M. S., & Hossain, M. S. (2026) · American Journal of Smart Technology and Solutions · doi:10.54536/ajsts.v5i2.8842

Neural network and machine learning-based analysis of clinical associated features in breast cancer pathogenesis

Yang, L., & Wang, W. (2026) · Technology and Health Care · doi:10.1177/09287329261505770

From Black Box to Glass Box: A Survey of Explainable AI in Mammographic Screening

Hashim, S. M., & Kutucu, H. (2026) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis...1766498

Beyond the black box: lessons in explainability from AI in mammography

Ciardiello, A., D'Angelo, A. R., Angelis, L. D., Giagu, S., Sala, E., & Gigante, G. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11518-5 · Cited by 3 · see citing works

Explainable AI for Mammography: A Review

Kumar, V. V., & Arunkumar, T. R. (2025) · Indian Journal of Science and Technology · doi:10.17485/ijst/v18i46.1193

Explainable Artificial Intelligence in Mammography: A Systematic Review of Methods, Evaluation Practices, and Clinical Readiness

Pesapane, F., Rotili, A., Penco, S., Dominelli, V., Priolo, F., Marinucci, I., Nicosia, L., Grasso, R., Pravettoni, G., & Cassano, E. (2026) · Diagnostics · doi:10.3390/diagnostics16091412 · Cited by 3 · see citing works

Explainable Artificial Intelligence Methods for Breast Cancer Recognition

Damaševičius, R. (2024) · Innovation Discovery · doi:10.53964/id.2024025 · Cited by 10 · see citing works