Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence
Simple Summary Risk assessment in early breast cancer is critical for clinical decisions, but
defining risk categories poses a significant challenge. The integration of conventional …
defining risk categories poses a significant challenge. The integration of conventional …
Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer
C McCaffrey, C Jahangir, C Murphy… - Expert Review of …, 2024 - Taylor & Francis
Introduction Histological images contain phenotypic information predictive of patient
outcomes. Due to the heavy workload of pathologists, the time-consuming nature of …
outcomes. Due to the heavy workload of pathologists, the time-consuming nature of …
Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers
A Katayama, Y Aoki, Y Watanabe, J Horiguchi… - International Journal of …, 2024 - Springer
Breast cancer is the most prevalent cancer among women, and its diagnosis requires the
accurate identification and classification of histological features for effective patient …
accurate identification and classification of histological features for effective patient …
[HTML][HTML] Characterization of Breast Cancer Intra-Tumor Heterogeneity Using Artificial Intelligence
Intra-tumor heterogeneity (ITH) is a fundamental characteristic of breast cancer (BC),
influencing tumor progression, prognosis, and therapeutic responses. However, the …
influencing tumor progression, prognosis, and therapeutic responses. However, the …
Bias reduction using combined stain normalization and augmentation for AI-based classification of histological images
C Franchet, R Schwob, G Bataillon, C Syrykh… - Computers in Biology …, 2024 - Elsevier
Artificial intelligence (AI)-assisted diagnosis is an ongoing revolution in pathology. However,
a frequent drawback of AI models is their propension to make decisions based rather on …
a frequent drawback of AI models is their propension to make decisions based rather on …
GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology
RK Mondol, EKA Millar, PH Graham, L Browne… - arXiv preprint arXiv …, 2025 - arxiv.org
Explainable AI (XAI) in medical histopathology is essential for enhancing the interpretability
and clinical trustworthiness of deep learning models in cancer diagnosis. However, the …
and clinical trustworthiness of deep learning models in cancer diagnosis. However, the …
Weakly Supervised Breast Cancer Classification on WSI Using Transformer and Graph Attention Network
M Li, B Zhang, J Sun, J Zhang, B Liu… - International Journal of …, 2024 - Wiley Online Library
Recently, multiple instance learning (MIL) has been successfully used in weakly supervised
breast cancer classification on whole‐slide imaging (WSI) and has become an important …
breast cancer classification on whole‐slide imaging (WSI) and has become an important …