[HTML][HTML] A review of uncertainty estimation and its application in medical imaging
The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …
importance. Deep learning has shown great promise in medical imaging, but the reliability …
[HTML][HTML] Revolutionizing digital pathology with the power of generative artificial intelligence and foundation models
Digital pathology has transformed the traditional pathology practice of analyzing tissue
under a microscope into a computer vision workflow. Whole slide imaging allows …
under a microscope into a computer vision workflow. Whole slide imaging allows …
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 …
Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review
Artificial Intelligence (AI) has demonstrated significant potential in automating various
medical imaging tasks, which could soon become routine in clinical practice for disease …
medical imaging tasks, which could soon become routine in clinical practice for disease …
Failure detection in deep neural networks for medical imaging
Deep neural networks (DNNs) have started to find their role in the modern healthcare
system. DNNs are being developed for diagnosis, prognosis, treatment planning, and …
system. DNNs are being developed for diagnosis, prognosis, treatment planning, and …
Exploring robust architectures for deep artificial neural networks
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve
their predictive performance. However, the relationship between the architecture of a DANN …
their predictive performance. However, the relationship between the architecture of a DANN …
Efficient scopeformer: Toward scalable and rich feature extraction for intracranial hemorrhage detection
The quality and richness of feature maps extracted by convolution neural networks (CNNs)
and vision Transformers (ViTs) directly relate to the robust model performance. In medical …
and vision Transformers (ViTs) directly relate to the robust model performance. In medical …
Trustworthy uncertainty propagation for sequential time-series analysis in rnns
D Dera, S Ahmed, NC Bouaynaya… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The massive time-series production through the Internet of Things and digital healthcare
requires novel data modeling and prediction. Recurrent neural networks (RNNs) are …
requires novel data modeling and prediction. Recurrent neural networks (RNNs) are …
EvalAttAI: a holistic approach to evaluating attribution maps in robust and non-robust models
The expansion of explainable artificial intelligence as a field of research has generated
numerous methods of visualizing and understanding the black box of a machine learning …
numerous methods of visualizing and understanding the black box of a machine learning …
Analytical uncertainty propagation in neural networks
P Jungmann, J Poray, A Kumar - IEEE Transactions on Neural …, 2024 - ieeexplore.ieee.org
The usage of machine-learning techniques, such as neural networks, is common in a large
variety of domains. Estimating the certainty of a predicted value is important when precise …
variety of domains. Estimating the certainty of a predicted value is important when precise …