Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches
Breast cancer is a significant global health burden, with increasing morbidity and mortality
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …
Radiomics in breast cancer: Current advances and future directions
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 …
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
Numerous patch-based methods have recently been proposed for histological image based
breast cancer classification. However, their performance could be highly affected by ignoring …
breast cancer classification. However, their performance could be highly affected by ignoring …
Prototype learning guided hybrid network for breast tumor segmentation in dce-mri
Automated breast tumor segmentation on the basis of dynamic contrast-enhancement
magnetic resonance imaging (DCE-MRI) has shown great promise in clinical practice …
magnetic resonance imaging (DCE-MRI) has shown great promise in clinical practice …
Breast fibroglandular tissue segmentation for automated BPE quantification with iterative cycle-consistent semi-supervised learning
Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-
Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast …
Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast …
[PDF][PDF] Tsesnet: Temporal-spatial enhanced breast tumor segmentation in dce-mri using feature perception and separability
Accurate segmentation of breast tumors in dynamic contrast-enhanced magnetic resonance
images (DCE-MRI) is critical for early diagnosis of breast cancer. However, this task remains …
images (DCE-MRI) is critical for early diagnosis of breast cancer. However, this task remains …
Performance of an AI-powered visualization software platform for precision surgery in breast cancer patients
M Weitz, JR Pfeiffer, S Patel, M Biancalana, A Pekis… - NPJ Breast …, 2024 - nature.com
Surgery remains the primary treatment modality in the management of early-stage invasive
breast cancer. Artificial intelligence (AI)-powered visualization platforms offer the compelling …
breast cancer. Artificial intelligence (AI)-powered visualization platforms offer the compelling …
A multi-scale, multi-task fusion UNet model for accurate breast tumor segmentation
S Dai, X Liu, W Wei, X Yin, L Qiao, J Wang… - Computer Methods and …, 2025 - Elsevier
Abstract Background and Objective: Breast cancer is the most common cancer type among
women worldwide and a leading cause of female death. Accurately interpreting these …
women worldwide and a leading cause of female death. Accurately interpreting these …
Modality-Specific Information Disentanglement From Multi-Parametric MRI for Breast Tumor Segmentation and Computer-Aided Diagnosis
Breast cancer is becoming a significant global health challenge, with millions of fatalities
annually. Magnetic Resonance Imaging (MRI) can provide various sequences for …
annually. Magnetic Resonance Imaging (MRI) can provide various sequences for …
[HTML][HTML] Edge of discovery: Enhancing breast tumor MRI analysis with boundary-driven deep learning
Manually segmenting breast lesion images poses a labor-intensive and expensive
undertaking for radiologists. Therefore, the adoption of an automated diagnostic approach …
undertaking for radiologists. Therefore, the adoption of an automated diagnostic approach …