Uncertainty quantification in multi‐class segmentation: Comparison between Bayesian and non‐Bayesian approaches in a clinical perspective
Background Automatic segmentation techniques based on Convolutional Neural Networks
(CNNs) are widely adopted to automatically identify any structure of interest from a medical …
(CNNs) are widely adopted to automatically identify any structure of interest from a medical …
Label-wise Aleatoric and Epistemic Uncertainty Quantification
We present a novel approach to uncertainty quantification in classification tasks based on
label-wise decomposition of uncertainty measures. This label-wise perspective allows …
label-wise decomposition of uncertainty measures. This label-wise perspective allows …
Disentangled uncertainty and out of distribution detection in medical generative models
K Lakara, M Valdenegro-Toro - arXiv preprint arXiv:2211.06250, 2022 - arxiv.org
Trusting the predictions of deep learning models in safety critical settings such as the
medical domain is still not a viable option. Distentangled uncertainty quantification in the …
medical domain is still not a viable option. Distentangled uncertainty quantification in the …
PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation
In the domain of medical imaging, many supervised learning based methods for
segmentation face several challenges such as high variability in annotations from multiple …
segmentation face several challenges such as high variability in annotations from multiple …
Uncertainty Quantification for Image-based Traffic Prediction across Cities
Despite the strong predictive performance of deep learning models for traffic prediction, their
widespread deployment in real-world intelligent transportation systems has been restrained …
widespread deployment in real-world intelligent transportation systems has been restrained …
Credal Wrapper of Model Averaging for Uncertainty Estimation on Out-Of-Distribution Detection
This paper presents an innovative approach, called credal wrapper, to formulating a credal
set representation of model averaging for Bayesian neural networks (BNNs) and deep …
set representation of model averaging for Bayesian neural networks (BNNs) and deep …
A study of Bayesian deep network uncertainty and its application to synthetic CT generation for MR‐only radiotherapy treatment planning
Background The use of synthetic computed tomography (CT) for radiotherapy treatment
planning has received considerable attention because of the absence of ionizing radiation …
planning has received considerable attention because of the absence of ionizing radiation …
A comprehensive study on the prediction reliability of graph neural networks for virtual screening
Prediction models based on deep neural networks are increasingly gaining attention for fast
and accurate virtual screening systems. For decision makings in virtual screening …
and accurate virtual screening systems. For decision makings in virtual screening …
SkiNet: a deep learning solution for skin lesion diagnosis with uncertainty estimation and Explainability
Skin cancer is considered to be the most common human malignancy. Around 5 million new
cases of skin cancer are recorded in the United States annually. Early identification and …
cases of skin cancer are recorded in the United States annually. Early identification and …
Quantifying Uncertainty in Deep Learning Classification with Noise in Discrete Inputs for Risk-Based Decision Making
M Kheirandish, S Zhang, DG Catanzaro… - arXiv preprint arXiv …, 2023 - arxiv.org
The use of Deep Neural Network (DNN) models in risk-based decision-making has attracted
extensive attention with broad applications in medical, finance, manufacturing, and quality …
extensive attention with broad applications in medical, finance, manufacturing, and quality …