Aug 2026· Network Modeling Analysis in Health Informatics and Bioinformatics· Vol 15· 0 citations· 85 references
TL;DR
This systematic review synthesizes hybrid deep learning methods across mammography, ultrasound, MRI, histopathology/whole-slide imaging, and clinically oriented multimodal settings and provides practical reporting and evaluation recommendations to support more reliable and clinically ready systems.
Artificial intelligence shows substantial potential to enhance breast cancer imaging, but broader clinical translation requires robust external and prospective validation, improved calibration, assessment of generalizability and bias, and integration into clinical workflows.
I. Khan, Syed Taimoor Hussain Shah, Alexandra Tsipourakis et al.· Frontiers in Imaging· 0 citations
Background: Class imbalance remains a central methodological obstacle in machine learning (ML)-based breast cancer prediction, where the clinically critical minority class is systematically underrepresented relative to the majority class. A systematic review by Ghavidel and Pazos synthesized ML approaches to this probl...
Omega John Unogwu, Andrea Ngohide Abaagu, Ankar Tersoo Catherine· EDRAAK· 0 citations
Introduction: Accurate detection of clinically significant prostate cancer (csPCa) is critical for optimizing treatment strategies and reducing overdiagnosis of indolent disease. Artificial intelligence (AI), particularly deep learning algorithms integrated with multiparametric magnetic resonance imaging (mpMRI) and di...
Anjas Anhar Prastowo, M. Ali· World Journal of Advanced Re...· 0 citations
Breast cancer is one of the most common cancers in women worldwide affecting approximately 2.3 million women annually and causing 685,000 deaths each year. Thanks to screening and treatment, the 5-year survival rate exceeds 90% when detected early. However, recurrence remains a major challenge, occurring in 20-30% of c...
Ahlam Ait Yahia, Ichrak Khoulqi, N. Idrissi· EPJ Web of Conferences· 0 citations
Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, underscoring the importance of early and accurate diagnosis to improve patient outcomes. Magnetic resonance imaging (MRI) is a highly sensitive imaging modality for detecting breast malignancies, particularly in patients...
Qais Al-Azzam, W. Balachandran, Ziad Hunaiti· AI in Medicine· 0 citations
Breast cancer remains the most frequently diagnosed cancer and a leading cause of cancer death among women worldwide, so tools that support early and accurate detection are urgently needed. This review synthesizes a pool of one hundred studies, largely published between 2023 and 2026, on deep learning, ensemble learnin...
Abraham Temilade Olumide, Obe Olumide Olayinka, Akinwonmi Akintoba Emmanuel et al.· INTERNATIONAL JOURNAL OF MAT...· 0 citations
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