Knowledge graphs: Opportunities and challenges
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …
important to organize and represent the enormous volume of knowledge appropriately. As …
Deep learning in cancer diagnosis, prognosis and treatment selection
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …
technique called artificial neural networks to extract patterns and make predictions from …
Metabolomic profiles predict individual multidisease outcomes
Risk stratification is critical for the early identification of high-risk individuals and disease
prevention. Here we explored the potential of nuclear magnetic resonance (NMR) …
prevention. Here we explored the potential of nuclear magnetic resonance (NMR) …
Multimodal co-attention transformer for survival prediction in gigapixel whole slide images
Survival outcome prediction is a challenging weakly-supervised and ordinal regression task
in computational pathology that involves modeling complex interactions within the tumor …
in computational pathology that involves modeling complex interactions within the tumor …
Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
Traditional image-based survival prediction models rely on discriminative patch labeling
which make those methods not scalable to extend to large datasets. Recent studies have …
which make those methods not scalable to extend to large datasets. Recent studies have …
Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival
A Moncada-Torres, MC van Maaren, MP Hendriks… - Scientific reports, 2021 - nature.com
Abstract Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in
oncology. Recently, several machine learning (ML) techniques have been adapted for this …
oncology. Recently, several machine learning (ML) techniques have been adapted for this …
Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images
V Andrearczyk, V Oreiller, S Boughdad… - 3D head and neck tumor …, 2021 - Springer
This paper presents an overview of the second edition of the HEad and neCK TumOR
(HECKTOR) challenge, organized as a satellite event of the 24th International Conference …
(HECKTOR) challenge, organized as a satellite event of the 24th International Conference …
[HTML][HTML] An overview of deep learning in medical imaging focusing on MRI
AS Lundervold, A Lundervold - Zeitschrift für Medizinische Physik, 2019 - Elsevier
What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective
In the Engineering discipline, prognostics play an essential role in improving system safety,
reliability and enabling predictive maintenance decision-making. Due to the adoption of …
reliability and enabling predictive maintenance decision-making. Due to the adoption of …