[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 …
PremiUm-CNN: Propagating uncertainty towards robust convolutional neural networks
Deep neural networks (DNNs) have surpassed human-level accuracy in various learning
tasks. However, unlike humans who have a natural cognitive intuition for probabilities, DNNs …
tasks. However, unlike humans who have a natural cognitive intuition for probabilities, DNNs …
Probabilistic predictions with federated learning
Probabilistic predictions with machine learning are important in many applications. These
are commonly done with Bayesian learning algorithms. However, Bayesian learning …
are commonly done with Bayesian learning algorithms. However, Bayesian learning …
Revisiting the fragility of influence functions
In the last few years, many works have tried to explain the predictions of deep learning
models. Few methods, however, have been proposed to verify the accuracy or faithfulness of …
models. Few methods, however, have been proposed to verify the accuracy or faithfulness of …
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 …
Trustworthy uncertainty propagation for sequential time-series analysis in rnns
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 …
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 …
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 …
Bayes-SAR net: Robust SAR image classification with uncertainty estimation using bayesian convolutional neural network
Synthetic aperture radar (SAR) image classification is a challenging problem due to the
complex imaging mechanism as well as the random speckle noise, which affects radar …
complex imaging mechanism as well as the random speckle noise, which affects radar …