AIMC Topic: Uncertainty

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Uncertainty-aware deep learning for trustworthy prediction of long-term outcome after endovascular thrombectomy.

Scientific reports
Acute ischemic stroke (AIS) is a leading global cause of mortality and morbidity. Improving long-term outcome predictions after thrombectomy can enhance treatment quality by supporting clinical decision-making. With the advent of interpretable deep l...

Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis.

Artificial intelligence in medicine
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. End users are particularly reluctant to rely on the opaque predictions of DL mo...

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome.

BMC bioinformatics
BACKGROUND: Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome predict...

Spatial multi-attention conditional neural processes.

Neural networks : the official journal of the International Neural Network Society
Spatial prediction tasks are challenging when observed samples are sparse and prediction samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction tasks and have the advantage of measuring the uncertainty of the interpola...

All that Glitters Is not Gold: Type-I Error Controlled Variable Selection from Clinical Trial Data.

Clinical pharmacology and therapeutics
Clinical trials are primarily conducted to estimate causal effects, but the data collected can also be invaluable for additional research, such as identifying prognostic measures of disease or biomarkers that predict treatment efficacy. However, thes...

Segment anything model for medical image segmentation: Current applications and future directions.

Computers in biology and medicine
Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision. The recent introduction of the Segment Anything Model (SAM) signifies a noteworthy...

U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging.

Computers in biology and medicine
With the increasing prevalence of machine learning in critical fields like healthcare, ensuring the safety and reliability of these systems is crucial. Estimating uncertainty plays a vital role in enhancing reliability by identifying areas of high an...

How to evaluate uncertainty estimates in machine learning for regression?

Neural networks : the official journal of the International Neural Network Society
As neural networks become more popular, the need for accompanying uncertainty estimates increases. There are currently two main approaches to test the quality of these estimates. Most methods output a density. They can be compared by evaluating their...

InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography.

BMC medical informatics and decision making
BACKGROUND: This study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (PPG) signals for sleep monito...

Unraveling the impact of digital transformation on green innovation through microdata and machine learning.

Journal of environmental management
How to use digitalization to support the green transformation of organizations has drawn much attention based on the rapid development of digitalization. However, digital transformation (DT) may be hindered by the "IT productivity paradox." Exploring...