Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches

J Zhang, J Wu, XS Zhou, F Shi, D Shen - Seminars in Cancer Biology, 2023 - Elsevier
Breast cancer is a significant global health burden, with increasing morbidity and mortality
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …

Radiomics in breast cancer: Current advances and future directions

YJ Qi, GH Su, C You, X Zhang, Y **ao, YZ Jiang… - Cell Reports …, 2024 - cell.com
Breast cancer is a common disease that causes great health concerns to women worldwide.
During the diagnosis and treatment of breast cancer, medical imaging plays an essential …

A hierarchical graph V-Net with semi-supervised pre-training for histological image based breast Cancer classification

Y Li, Y Shen, J Zhang, S Song, Z Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Numerous patch-based methods have recently been proposed for histological image based
breast cancer classification. However, their performance could be highly affected by ignoring …

[HTML][HTML] Edge of discovery: Enhancing breast tumor MRI analysis with boundary-driven deep learning

NU Rehman, J Wang, H Weiyan, I Ali, A Akbar… - … Signal Processing and …, 2024 - Elsevier
Manually segmenting breast lesion images poses a labor-intensive and expensive
undertaking for radiologists. Therefore, the adoption of an automated diagnostic approach …

The Smart Performance Comparison of AI-based Breast Cancer Detection Models

S Samreen, AS Mohammed… - 2024 International …, 2024 - ieeexplore.ieee.org
The smart performance comparison of AI-based breast cancer detection models is an
important research topic in the healthcare industry. It is used to compare and evaluate …

Prototype learning guided hybrid network for breast tumor segmentation in dce-mri

L Zhou, Y Zhang, J Zhang, X Qian… - … on Medical Imaging, 2024 - ieeexplore.ieee.org
Automated breast tumor segmentation on the basis of dynamic contrast-enhancement
magnetic resonance imaging (DCE-MRI) has shown great promise in clinical practice …

Modality-specific information disentanglement from multi-parametric MRI for breast tumor segmentation and computer-aided diagnosis

Q Chen, J Zhang, R Meng, L Zhou, Z Li… - … on Medical Imaging, 2024 - ieeexplore.ieee.org
Breast cancer is becoming a significant global health challenge, with millions of fatalities
annually. Magnetic Resonance Imaging (MRI) can provide various sequences for …

Evaluating magnetic seed localization in targeted axillary dissection for node-positive early breast cancer patients receiving neoadjuvant systemic therapy: a …

M Alamoodi, U Wazir, RA Sakr… - Journal of Clinical …, 2024 - mdpi.com
Background/Objectives: De-escalation of axillary surgery is made possible by
advancements in both neoadjuvant systemic therapy (NST) and in localisation technology …

Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Using a Longitudinally Aware Segmentation Network

X Tie, M Shin, C Lee, SB Perlman… - Radiology: Artificial …, 2025 - pubs.rsna.org
“Just Accepted” papers have undergone full peer review and have been accepted for
publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout …

Breast fibroglandular tissue segmentation for automated BPE quantification with iterative cycle-consistent semi-supervised learning

J Zhang, Z Cui, L Zhou, Y Sun, Z Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-
Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast …