Add to ORKGMy thesis includes 4 independent chapters, and their topics are novel uncertainty quantification methods for medical image segmentation and reconstruction, a robust deep model for survival analysis, and a novel gray box adversarial defense. The thesis has a mix of theoretical and empirical results, and contains two applications in healthcare, which are medical image analysis and survival analysis
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential la...
Mistrust is a major barrier to implementing deep learning in healthcare settings. Entrustment could ...
The field of medical artificial intelligence (AI) has seen significant advancements with the availab...
As machine learning systems become increasingly complex and autonomous, the integration of uncertain...
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to...
Despite the recent improvements in overall accuracy, deep learning systems still exhibit low levels ...
Abstract Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of unce...
Deep learning is now ubiquitous in the research field of medical image computing. As such technologi...
This thesis presents an uncertainty quantification (UQ) system on medical classification imaging tas...
This thesis focuses on improving accuracy and assessing robustness of deep learning for medical imag...
Software-intensive systems that rely on machine learning (ML) and artificial intelligence (AI) are i...
The Intensive Care Unit (ICU) is a hospital department where machine learning has the potential to p...
Deep learning algorithms have the potential to automate the examination of medical images obtained i...
The application of deep learning to the medical diagnosis process has been an active area of researc...
Deep learning models have achieved tremendous successes in accurate predictions for computer vision,...
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential la...
Mistrust is a major barrier to implementing deep learning in healthcare settings. Entrustment could ...
The field of medical artificial intelligence (AI) has seen significant advancements with the availab...
As machine learning systems become increasingly complex and autonomous, the integration of uncertain...
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to...
Despite the recent improvements in overall accuracy, deep learning systems still exhibit low levels ...
Abstract Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of unce...
Deep learning is now ubiquitous in the research field of medical image computing. As such technologi...
This thesis presents an uncertainty quantification (UQ) system on medical classification imaging tas...
This thesis focuses on improving accuracy and assessing robustness of deep learning for medical imag...
Software-intensive systems that rely on machine learning (ML) and artificial intelligence (AI) are i...
The Intensive Care Unit (ICU) is a hospital department where machine learning has the potential to p...
Deep learning algorithms have the potential to automate the examination of medical images obtained i...
The application of deep learning to the medical diagnosis process has been an active area of researc...
Deep learning models have achieved tremendous successes in accurate predictions for computer vision,...
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential la...
Mistrust is a major barrier to implementing deep learning in healthcare settings. Entrustment could ...
The field of medical artificial intelligence (AI) has seen significant advancements with the availab...