Artificial intelligence for multimodal data integration in oncology
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …
from radiology, histology, and genomics to electronic health records. Current artificial …
Advances in spatial transcriptomic data analysis
Spatial transcriptomics is a rapidly growing field that promises to comprehensively
characterize tissue organization and architecture at the single-cell or subcellular resolution …
characterize tissue organization and architecture at the single-cell or subcellular resolution …
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Several studies underscore the potential of deep learning in identifying complex patterns,
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of
approaches have been proposed. However, only a few studies have comprehensively …
approaches have been proposed. However, only a few studies have comprehensively …
Virtual alignment of pathology image series for multi-gigapixel whole slide images
CD Gatenbee, AM Baker, S Prabhakaran… - Nature …, 2023 - nature.com
Interest in spatial omics is on the rise, but generation of highly multiplexed images remains
challenging, due to cost, expertise, methodical constraints, and access to technology. An …
challenging, due to cost, expertise, methodical constraints, and access to technology. An …
A survey on artificial intelligence in histopathology image analysis
The increasing adoption of the whole slide image (WSI) technology in histopathology has
dramatically transformed pathologists' workflow and allowed the use of computer systems in …
dramatically transformed pathologists' workflow and allowed the use of computer systems in …
Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images
Advances in computational algorithms and tools have made the prediction of cancer patient
outcomes using computational pathology feasible. However, predicting clinical outcomes …
outcomes using computational pathology feasible. However, predicting clinical outcomes …
Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification
Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is
broadly used in diagnostic pathology laboratories for patient care. So far, however, clinical …
broadly used in diagnostic pathology laboratories for patient care. So far, however, clinical …
A comprehensive review of federated learning for COVID‐19 detection
Abstract The coronavirus of 2019 (COVID‐19) was declared a global pandemic by World
Health Organization in March 2020. Effective testing is crucial to slow the spread of the …
Health Organization in March 2020. Effective testing is crucial to slow the spread of the …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …