Applications of microwaves in medicine leveraging artificial intelligence: Future perspectives
Microwaves are non-ionizing electromagnetic radiation with waves of electrical and
magnetic energy transmitted at different frequencies. They are widely used in various …
magnetic energy transmitted at different frequencies. They are widely used in various …
Clinical applications of graph neural networks in computational histopathology: A review
X Meng, T Zou - Computers in Biology and Medicine, 2023 - Elsevier
Pathological examination is the optimal approach for diagnosing cancer, and with the
advancement of digital imaging technologies, it has spurred the emergence of …
advancement of digital imaging technologies, it has spurred the emergence of …
Sparse multi-modal graph transformer with shared-context processing for representation learning of giga-pixel images
Processing giga-pixel whole slide histopathology images (WSI) is a computationally
expensive task. Multiple instance learning (MIL) has become the conventional approach to …
expensive task. Multiple instance learning (MIL) has become the conventional approach to …
MamlFormer: Priori-experience guiding transformer network via manifold adversarial multi-modal learning for laryngeal histopathological grading
Pathologic grading of laryngeal squamous cell carcinoma (LSCC) plays a crucial role in
diagnosis, prognosis, and migration. However, the grading performance and interpretability …
diagnosis, prognosis, and migration. However, the grading performance and interpretability …
Graph ai in medicine
In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks (GNNs), stands out for its capability to capture intricate relationships within …
neural networks (GNNs), stands out for its capability to capture intricate relationships within …
Shared-specific feature learning with bottleneck fusion transformer for multi-modal whole slide image analysis
Z Wang, L Yu, X Ding, X Liao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The fusion of multi-modal medical data is essential to assist medical experts to make
treatment decisions for precision medicine. For example, combining the whole slide …
treatment decisions for precision medicine. For example, combining the whole slide …
MUMA: A multi-omics meta-learning algorithm for data interpretation and classification
Multi-omics data integration is a promising field combining various types of omics data, such
as genomics, transcriptomics, and proteomics, to comprehensively understand the …
as genomics, transcriptomics, and proteomics, to comprehensively understand the …
[HTML][HTML] Multi-scale relational graph convolutional network for multiple instance learning in histopathology images
Graph convolutional neural networks have shown significant potential in natural and
histopathology images. However, their use has only been studied in a single magnification …
histopathology images. However, their use has only been studied in a single magnification …
Improving the diagnosis of skin biopsies using tissue segmentation
Invasive melanoma, a common type of skin cancer, is considered one of the deadliest.
Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if …
Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if …
Amigo: sparse multi-modal graph transformer with shared-context processing for representation learning of giga-pixel images
Processing giga-pixel whole slide histopathology images (WSI) is a computationally
expensive task. Multiple instance learning (MIL) has become the conventional approach to …
expensive task. Multiple instance learning (MIL) has become the conventional approach to …