Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability
0
Zitationen
8
Autoren
2026
Jahr
Abstract
: AI constitutes a pivotal factor in facilitating early BC diagnosis and optimizing treatment outcomes. Nevertheless, significant challenges persist, including dataset heterogeneity, model generalizability, standardization of imaging protocols, computational resource limitations, and the seamless integration of these technologies into established clinical workflows. Future research must prioritize robust multi-dataset validation and standardized implementation frameworks to overcome existing limitations and advance successful BC diagnostic practices.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 14.099 Zit.
pROC: an open-source package for R and S+ to analyze and compare ROC curves
2011 · 13.855 Zit.
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.577 Zit.
A survey on Image Data Augmentation for Deep Learning
2019 · 12.211 Zit.
QuPath: Open source software for digital pathology image analysis
2017 · 8.485 Zit.