Bioinspired Self‐healing Soft Electronics
Inspired by nature, various self‐healing materials that can recover their physical properties
after external damage have been developed. Recently, self‐healing materials have been …
after external damage have been developed. Recently, self‐healing materials have been …
Artificial intelligence for digital and computational pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …
including deep learning, have boosted the field of computational pathology. This field holds …
Machine learning methods for cancer classification using gene expression data: A review
F Alharbi, A Vakanski - Bioengineering, 2023 - mdpi.com
Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells
that can spread in different parts of the body. According to the World Health Organization …
that can spread in different parts of the body. According to the World Health Organization …
Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology
Z Wang, Y Liu, X Niu - Seminars in Cancer Biology, 2023 - Elsevier
Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently,
artificial intelligence approaches, particularly machine learning and deep learning, are …
artificial intelligence approaches, particularly machine learning and deep learning, are …
Quantitative spatial profiling of immune populations in pancreatic ductal adenocarcinoma reveals tumor microenvironment heterogeneity and prognostic biomarkers
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with poor 5-year
survival rates, necessitating identification of novel therapeutic targets. Elucidating the …
survival rates, necessitating identification of novel therapeutic targets. Elucidating the …
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 …
Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group …
DB Page, G Broeckx, CA Jahangir… - The Journal of …, 2023 - Wiley Online Library
Modern histologic imaging platforms coupled with machine learning methods have provided
new opportunities to map the spatial distribution of immune cells in the tumor …
new opportunities to map the spatial distribution of immune cells in the tumor …
Co-pilot: Dynamic top-down point cloud with conditional neighborhood aggregation for multi-gigapixel histopathology image representation
Predicting survival rates based on multi-gigapixel histopathology images is one of the most
challenging tasks in digital pathology. Due to the computational complexities, Multiple …
challenging tasks in digital pathology. Due to the computational complexities, Multiple …
[HTML][HTML] Multimodal data integration for oncology in the era of deep neural networks: a review
Cancer research encompasses data across various scales, modalities, and resolutions, from
screening and diagnostic imaging to digitized histopathology slides to various types of …
screening and diagnostic imaging to digitized histopathology slides to various types of …
Graph convolutional networks for multi-modality medical imaging: Methods, architectures, and clinical applications
Image-based characterization and disease understanding involve integrative analysis of
morphological, spatial, and topological information across biological scales. The …
morphological, spatial, and topological information across biological scales. The …