[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges

M Abdar, F Pourpanah, S Hussain, D Rezazadegan… - Information fusion, 2021 - Elsevier
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Data augmentation for brain-tumor segmentation: a review

J Nalepa, M Marcinkiewicz, M Kawulok - Frontiers in computational …, 2019 - frontiersin.org
Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …

A survey of uncertainty in deep neural networks

J Gawlikowski, CRN Tassi, M Ali, J Lee, M Humt… - Artificial Intelligence …, 2023 - Springer
Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images

C Srinivas, NP KS, M Zakariah… - Journal of …, 2022 - Wiley Online Library
Brain tumor classification is a very important and the most prominent step for assessing life‐
threatening abnormal tissues and providing an efficient treatment in patient recovery. To …

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

S Bakas, M Reyes, A Jakab, S Bauer… - arxiv preprint arxiv …, 2018 - arxiv.org
Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …

Attention gate resU-Net for automatic MRI brain tumor segmentation

J Zhang, Z Jiang, J Dong, Y Hou, B Liu - IEEE Access, 2020 - ieeexplore.ieee.org
Brain tumor segmentation technology plays a pivotal role in the process of diagnosis and
treatment of MRI brain tumors. It helps doctors to locate and measure tumors, as well as …

U-Net-based models towards optimal MR brain image segmentation

R Yousef, S Khan, G Gupta, T Siddiqui, BM Albahlal… - Diagnostics, 2023 - mdpi.com
Brain tumor segmentation from MRIs has always been a challenging task for radiologists,
therefore, an automatic and generalized system to address this task is needed. Among all …

Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation

G Wang, W Li, S Ourselin… - Frontiers in computational …, 2019 - frontiersin.org
Automatic segmentation of brain tumors from medical images is important for clinical
assessment and treatment planning of brain tumors. Recent years have seen an increasing …

Brain tumor detection and multi-grade segmentation through hybrid caps-VGGNet model

A Jabbar, S Naseem, T Mahmood, T Saba… - IEEE …, 2023 - ieeexplore.ieee.org
Around the world, brain tumors are becoming the leading cause of mortality. The inability to
undertake a timely tumor diagnosis is the primary cause of this pandemic. Brain cancer …

RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames

MU Rehman, J Ryu, IF Nizami, KT Chong - Computers in Biology and …, 2023 - Elsevier
Brain tumors are one of the most fatal cancers. Magnetic Resonance Imaging (MRI) is a non-
invasive method that provides multi-modal images containing important information …