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 …
Recent advances in deep-learning-enhanced photoacoustic imaging
Photoacoustic imaging (PAI), recognized as a promising biomedical imaging modality for
preclinical and clinical studies, uniquely combines the advantages of optical and ultrasound …
preclinical and clinical studies, uniquely combines the advantages of optical and ultrasound …
A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification
ON Oyelade, EA Irunokhai, H Wang - Scientific Reports, 2024 - nature.com
There is a wide application of deep learning technique to unimodal medical image analysis
with significant classification accuracy performance observed. However, real-world …
with significant classification accuracy performance observed. However, real-world …
A multimodal breast cancer diagnosis method based on Knowledge-Augmented Deep Learning
D Guo, C Lu, D Chen, J Yuan, Q Duan, Z Xue… - … Signal Processing and …, 2024 - Elsevier
Breast cancer is a worldwide medical challenge that requires Early diagnosis. While there
are numerous diagnostic methods for breast cancer, many primarily focus on network …
are numerous diagnostic methods for breast cancer, many primarily focus on network …
Collaborative multi-modal deep learning and radiomic features for classification of strokes within 6 h
Clinicians use imaging-based acute stroke onset time (SOT) to make crucial decisions
regarding stroke treatments, such as thrombolysis or thrombectomy. Patients may receive …
regarding stroke treatments, such as thrombolysis or thrombectomy. Patients may receive …
Multimodal deep learning approaches for precision oncology: a comprehensive review
H Yang, M Yang, J Chen, G Yao, Q Zou… - Briefings in …, 2025 - academic.oup.com
The burgeoning accumulation of large-scale biomedical data in oncology, alongside
significant strides in deep learning (DL) technologies, has established multimodal DL (MDL) …
significant strides in deep learning (DL) technologies, has established multimodal DL (MDL) …
A review of cancer data fusion methods based on deep learning
With advancements in modern medical technology, an increasing amount of cancer-related
information can be acquired through various means, such as genomics, proteomics …
information can be acquired through various means, such as genomics, proteomics …
FAMF-Net: Feature Alignment Mutual Attention Fusion with Region Awareness for Breast Cancer Diagnosis via Imbalanced Data
Y Liu, J Li, C Zhao, Y Zhang, Q Chen… - … on Medical Imaging, 2024 - ieeexplore.ieee.org
Automatic and accurate classification of breast cancer in multimodal ultrasound images is
crucial to improve patients' diagnosis and treatment effect and save medical resources …
crucial to improve patients' diagnosis and treatment effect and save medical resources …
A multi-task model for reliable classification of thyroid nodules in ultrasound images
G Xing, Z Miao, Y Zheng, M Zhao - Biomedical Engineering Letters, 2024 - Springer
Thyroid nodules are common, and patients with potential malignant lesions are usually
diagnosed using ultrasound imaging to determine further treatment options. This study aims …
diagnosed using ultrasound imaging to determine further treatment options. This study aims …
[HTML][HTML] Advanced technologies for biomedical applications by emerging researchers in Asia‐Pacific
Over the past few decades, there have been numerous breakthroughs in advanced
technologies for biomedical applications. These advancements have formed the foundation …
technologies for biomedical applications. These advancements have formed the foundation …