Wenqi Li

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King's College London

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Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation

Multi-modal medical image segmentation plays an essential role in clinic...
01/05/2021 ∙ by Jingkun Chen, et al. ∙ 0 ∙ share

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Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan

The recent outbreak of COVID-19 has led to urgent needs for reliable dia...
11/23/2020 ∙ by Dong Yang, et al. ∙ 5 ∙ share

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LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation

Deep Learning (DL) models are becoming larger, because the increase in m...
06/22/2020 ∙ by Wentao Zhu, et al. ∙ 0 ∙ share

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Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA

Knowledge graph models world knowledge as concepts, entities, and the re...
03/09/2020 ∙ by Xianpei Han, et al. ∙ 0 ∙ share

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NeurReg: Neural Registration and Its Application to Image Segmentation

Registration is a fundamental task in medical image analysis which can b...
10/04/2019 ∙ by Wentao Zhu, et al. ∙ 20 ∙ share

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Privacy-preserving Federated Brain Tumour Segmentation

Due to medical data privacy regulations, it is often infeasible to colle...
10/02/2019 ∙ by Wenqi Li, et al. ∙ 18 ∙ share

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Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets

Brain tissue segmentation from multimodal MRI is a key building block of...
07/07/2019 ∙ by Reuben Dorent, et al. ∙ 2 ∙ share

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Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss

Automatic segmentation of vestibular schwannoma (VS) tumors from magneti...
06/10/2019 ∙ by Guotai Wang, et al. ∙ 2 ∙ share

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2017 Robotic Instrument Segmentation Challenge

In mainstream computer vision and machine learning, public datasets such...
02/18/2019 ∙ by Max Allan, et al. ∙ 14 ∙ share

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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different d...
11/05/2018 ∙ by Spyridon Bakas, et al. ∙ 0 ∙ share

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Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis...
10/18/2018 ∙ by Guotai Wang, et al. ∙ 6 ∙ share

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Deep Boosted Regression for MR to CT Synthesis

Attenuation correction is an essential requirement of positron emission ...
08/22/2018 ∙ by Kerstin Kläser, et al. ∙ 2 ∙ share

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Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks

Despite the state-of-the-art performance for medical image segmentation,...
07/19/2018 ∙ by Guotai Wang, et al. ∙ 12 ∙ share

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Test-time augmentation with uncertainty estimation for deep learning-based medical image segmentation

Data augmentation has been widely used for training deep learning system...
07/19/2018 ∙ by Guotai Wang, et al. ∙ 2 ∙ share

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Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration

One of the fundamental challenges in supervised learning for multimodal ...
07/09/2018 ∙ by Yipeng Hu, et al. ∙ 8 ∙ share

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Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning

Multi-task neural network architectures provide a mechanism that jointly...
06/18/2018 ∙ by Felix J. S. Bragman, et al. ∙ 2 ∙ share

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Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia

In this work, we have concentrated our efforts on the interpretability o...
05/02/2018 ∙ by Luis C. Garcia-Peraza-Herrera, et al. ∙ 0 ∙ share

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Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

Convolutional neural networks (CNNs) have achieved state-of-the-art perf...
10/11/2017 ∙ by Guotai Wang, et al. ∙ 0 ∙ share

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NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are i...
09/11/2017 ∙ by Eli Gibson, et al. ∙ 0 ∙ share

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Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

Deep-learning has proved in recent years to be a powerful tool for image...
07/11/2017 ∙ by Carole H Sudre, et al. ∙ 0 ∙ share

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On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

Deep convolutional neural networks are powerful tools for learning visua...
07/06/2017 ∙ by Wenqi Li, et al. ∙ 0 ∙ share

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DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

Accurate medical image segmentation is essential for diagnosis, surgical...
07/03/2017 ∙ by Guotai Wang, et al. ∙ 0 ∙ share

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Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks

The Dice score is widely used for binary segmentation due to its robustn...
07/03/2017 ∙ by Lucas Fidon, et al. ∙ 0 ∙ share

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ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools

Real-time tool segmentation from endoscopic videos is an essential part ...
06/25/2017 ∙ by Luis C. Garcia-Peraza-Herrera, et al. ∙ 0 ∙ share

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Scalable multimodal convolutional networks for brain tumour segmentation

Brain tumour segmentation plays a key role in computer-assisted surgery....
06/25/2017 ∙ by Lucas Fidon, et al. ∙ 0 ∙ share

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