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An Analysis of Loss Functions for Heavily Imbalanced Lesion Segmentation

Sensors (Basel, Switzerland), 2024-03, Vol.24 (6), p.1981

2024

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Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features

Medical image analysis, 2018-08, Vol.48, p.177-186

2018

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Deep Learning in Forestry Using UAV-Acquired RGB Data: A Practical Review

Remote sensing (Basel, Switzerland), 2021-07, Vol.13 (14), p.2837

2021

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Supervised Domain Adaptation for Automatic Sub-cortical Brain Structure Segmentation with Minimal User Interaction

Scientific reports, 2019-05, Vol.9 (1), p.6742-6742, Article 6742

2019

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A review of atlas-based segmentation for magnetic resonance brain images

Computer methods and programs in biomedicine, 2011-12, Vol.104 (3), p.e158-e177

2011

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Improving the detection of autism spectrum disorder by combining structural and functional MRI information

NeuroImage clinical, 2020-01, Vol.25, p.102181-102181, Article 102181

2020

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One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

NeuroImage clinical, 2019-01, Vol.21, p.101638-101638, Article 101638

2019

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A benchmark for 2D foetal brain ultrasound analysis

Scientific data, 2024-08, Vol.11 (1), p.923-10

2024

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Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Information sciences, 2012-03, Vol.186 (1), p.164-185

2012

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Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors

Neuroinformatics (Totowa, N.J.), 2021-07, Vol.19 (3), p.477-492

2021

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Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning

Sensors (Basel, Switzerland), 2021-01, Vol.21 (2), p.471

2021

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A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis

NeuroImage clinical, 2020-01, Vol.25, p.102149-102149, Article 102149

2020

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Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation

Frontiers in neuroscience, 2023-07, Vol.17, p.1196087-1196087

2023

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Detection of Invasive Species in Wetlands: Practical DL with Heavily Imbalanced Data

Remote sensing (Basel, Switzerland), 2020-10, Vol.12 (20), p.3431

2020

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A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis

NeuroImage clinical, 2018-01, Vol.17, p.607-615

2018

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Multiple Sclerosis Lesion Synthesis in MRI Using an Encoder-Decoder U-NET

IEEE access, 2019, Vol.7, p.25171-25184

2019

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Editorial: AI processing of UAV acquired images for pattern monitoring in natural and urban environments

Frontiers in robotics and AI, 2022-11, Vol.9, p.1053063-1053063

2022

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Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning

Frontiers in neuroscience, 2023-05, Vol.17, p.1167612

2023

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Quantitative Analysis of Patch-Based Fully Convolutional Neural Networks for Tissue Segmentation on Brain Magnetic Resonance Imaging

IEEE access, 2019, Vol.7, p.89986-90002

2019

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High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions

Frontiers in radiology, 2023-09, Vol.3, p.1238566-1238566

2023

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Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation

NeuroImage clinical, 2017-01, Vol.15, p.228-238

2017

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A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis

NPJ digital medicine, 2023-10, Vol.6 (1), p.196-196, Article 196

2023

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Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

NeuroImage (Orlando, Fla.), 2017-07, Vol.155, p.159-168

2017

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Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations

Journal of magnetic resonance imaging, 2015-01, Vol.41 (1), p.93-101

2015

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Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

Scientific reports, 2018-09, Vol.8 (1), p.13650-17, Article 13650

2018

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An Analysis of Loss Functions for Heavily Imbalanced Lesion Segmentation

Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features

Deep Learning in Forestry Using UAV-Acquired RGB Data: A Practical Review

Supervised Domain Adaptation for Automatic Sub-cortical Brain Structure Segmentation with Minimal User Interaction

A review of atlas-based segmentation for magnetic resonance brain images

Improving the detection of autism spectrum disorder by combining structural and functional MRI information

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

A benchmark for 2D foetal brain ultrasound analysis

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors

Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning

A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis

Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation

Detection of Invasive Species in Wetlands: Practical DL with Heavily Imbalanced Data

A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis

Multiple Sclerosis Lesion Synthesis in MRI Using an Encoder-Decoder U-NET

Editorial: AI processing of UAV acquired images for pattern monitoring in natural and urban environments

Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning

Quantitative Analysis of Patch-Based Fully Convolutional Neural Networks for Tissue Segmentation on Brain Magnetic Resonance Imaging

High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions

Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation

A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis

Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

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