Improving BI-RADS Mammographic Classification With Self-Supervised Vision Transformers and Cascade Learning

Improving BI-RADS Mammographic Classification With Self-Supervised Vision Transformers and Cascade Learning

Accurate and early breast cancer detection is critical for improving patient outcomes. In this study, we propose PatchCascade-ViT, a novel self-supervised Vision Transformer (ViT) framework for automated BI-RADS classification of mammographic images. Unlike conventional deep learning approaches that...

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Bibliographic Details
Main Authors: Abdelrahman Abdallah, Mahmoud Salaheldin Kasem, Ibrahim Abdelhalim, Norah Saleh Alghamdi, Ayman El-Baz
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Breast cancer
BI-RADS classification
self-supervised learning (SSL)
vision transformers (ViTs)
cascade classification
Online Access:https://ieeexplore.ieee.org/document/11045361/
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