[HTML][HTML] Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

L Vandewinckele, M Claessens, A Dinkla… - Radiotherapy and …, 2020 - Elsevier
Artificial Intelligence (AI) is currently being introduced into different domains, including
medicine. Specifically in radiation oncology, machine learning models allow automation and …

Deep learning based synthetic‐CT generation in radiotherapy and PET: a review

MF Spadea, M Maspero, P Zaffino, J Seco - Medical physics, 2021 - Wiley Online Library
Abstract Recently, deep learning (DL)‐based methods for the generation of synthetic
computed tomography (sCT) have received significant research attention as an alternative to …

Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation

T Nair, D Precup, DL Arnold, T Arbel - Medical image analysis, 2020 - Elsevier
Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …

[HTML][HTML] A review of uncertainty estimation and its application in medical imaging

K Zou, Z Chen, X Yuan, X Shen, M Wang, H Fu - Meta-Radiology, 2023 - Elsevier
The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

M Boulanger, JC Nunes, H Chourak, A Largent, S Tahri… - Physica Medica, 2021 - Elsevier
Purpose In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its
superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the …

Multi-task recurrent convolutional network with correlation loss for surgical video analysis

Y Jin, H Li, Q Dou, H Chen, J Qin, CW Fu… - Medical image analysis, 2020 - Elsevier
Surgical tool presence detection and surgical phase recognition are two fundamental yet
challenging tasks in surgical video analysis as well as very essential components in various …

[HTML][HTML] Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI

R Tanno, DE Worrall, E Kaden, A Ghosh, F Grussu… - NeuroImage, 2021 - Elsevier
Deep learning (DL) has shown great potential in medical image enhancement problems,
such as super-resolution or image synthesis. However, to date, most existing approaches …

Stochastic filter groups for multi-task cnns: Learning specialist and generalist convolution kernels

FJS Bragman, R Tanno, S Ourselin… - Proceedings of the …, 2019 - openaccess.thecvf.com
The performance of multi-task learning in Convolutional Neural Networks (CNNs) hinges on
the design of feature sharing between tasks within the architecture. The number of possible …

DeepCEST 3T: Robust MRI parameter determination and uncertainty quantification with neural networks—application to CEST imaging of the human brain at 3T

F Glang, A Deshmane, S Prokudin… - Magnetic resonance …, 2020 - Wiley Online Library
Purpose Calculation of sophisticated MR contrasts often requires complex mathematical
modeling. Data evaluation is computationally expensive, vulnerable to artifacts, and often …

Multi-task representation learning for pure exploration in bilinear bandits

S Mukherjee, Q Xie, J Hanna… - Advances in Neural …, 2024 - proceedings.neurips.cc
We study multi-task representation learning for the problem of pure exploration in bilinear
bandits. In bilinear bandits, an action takes theform of a pair of arms from two different entity …