Knowledge of performance (kp) is a type of feedback that can provide information about kin 170

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Highlights

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We provided an extensive review of uncertainty quantification methods in deep learning.

We covered popular and efficient Bayesian approaches for uncertainty quantification.

We listed notable ensemble techniques for quantifying uncertainty.

We discussed various applications of uncertainty quantification methods.

We summarized major open challenges and research gaps in uncertainty quantification.

Abstract

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes. They have been applied to solve a variety of real-world problems in science and engineering. Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions associated with UQ.

Keywords

Artificial intelligence

Uncertainty quantification

Deep learning

Machine learning

Bayesian statistics

Ensemble learning

© 2021 The Authors. Published by Elsevier B.V.

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Open access

Abstract

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

Abbreviations

ANN

Artificial Neural Network

SVM

Support Vector Machine

SSL

Semi-supervised Learning

TCGA

The Cancer Genome Atlas Research Network

HTT

High-throughput Technologies

OSCC

Oral Squamous Cell Carcinoma

CFS

Correlation based Feature Selection

ROC

Receiver Operating Characteristic

BCRSVM

Breast Cancer Support Vector Machine

PPI

Protein–Protein Interaction

GEO

Gene Expression Omnibus

LCS

Learning Classifying Systems

ES

Early Stopping algorithm

SEER

Surveillance, Epidemiology and End results Database

NSCLC

Non-small Cell Lung Cancer

NCI caArray

National Cancer Institute Array Data Management System

Keywords

Machine learning

Cancer susceptibility

Predictive models

Cancer recurrence

Cancer survival

Copyright © 2014 Published by Elsevier B.V.