3.1k citations · 10k across the 31 of their papers we have counts for
31 papers
StyleTokenizer: Defining Image Style by a Single Instance for Controlling Diffusion Models
Wen Li, Muyuan Fang, Cheng Zou +5
Despite the burst of innovative methods for controlling the diffusion process, effectively controlling image styles in text-to-image generation remains a challenging task. Many ada…
Syndrome-based Fusion Rules in Heterogeneous Distributed Quickest Change Detection
Wen-Hsuan Li, Yu-Chih Huang
In this paper, the heterogeneous distributed quickest change detection (HetDQCD) with 1-bit non-anonymous feedback is studied. The concept of syndromes is introduced and the family…
DC-Former: Diverse and Compact Transformer for Person Re-Identification
Wen Li, Cheng Zou, Meng Wang +5
In person re-identification (re-ID) task, it is still challenging to learn discriminative representation by deep learning, due to limited data. Generally speaking, the model will g…
Solutions for Fine-grained and Long-tailed Snake Species Recognition in SnakeCLEF 2022
Cheng Zou, Furong Xu, Meng Wang +2
Automatic snake species recognition is important because it has vast potential to help lower deaths and disabilities caused by snakebites. We introduce our solution in SnakeCLEF 20…
Bandwidth-Constrained Distributed Quickest Change Detection in Heterogeneous Sensor Networks: Anonymous vs Non-Anonymous Settings
Wen-Hsuan Li, Yu-Chih Huang
The heterogeneous distributed quickest change detection (HetDQCD) problem with 1-bit feedback is studied, in which a fusion center monitors an abrupt change through a bunch of hete…
HBReID: Harder Batch for Re-identification
Wen Li, Furong Xu, Jianan Zhao +4
Triplet loss is a widely adopted loss function in ReID task which pulls the hardest positive pairs close and pushes the hardest negative pairs far away. However, the selected sampl…
Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation
Jingkun Chen, Wenqi Li, Hongwei Li +1
Multi-modal medical image segmentation plays an essential role in clinical diagnosis. It remains challenging as the input modalities are often not well-aligned spatially. Existing…
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
Reuben Dorent, Thomas Booth, Wenqi Li +5
Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been de…
Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking
Luis C. García-Peraza-Herrera, Wenqi Li, Caspar Gruijthuijsen +7
Real-time tool segmentation is an essential component in computer-assisted surgical systems. We propose a novel real-time automatic method based on Fully Convolutional Networks (FC…
LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
Wentao Zhu, Can Zhao, Wenqi Li +3
Deep Learning (DL) models are becoming larger, because the increase in model size might offer significant accuracy gain. To enable the training of large deep networks, data paralle…
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Xianpei Han, Zhichun Wang, Jiangtao Zhang +22
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation…
The Future of Digital Health with Federated Learning
Nicola Rieke, Jonny Hancox, Wenqi Li +14
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern…
NeurReg: Neural Registration and Its Application to Image Segmentation
Wentao Zhu, Andriy Myronenko, Ziyue Xu +5
Registration is a fundamental task in medical image analysis which can be applied to several tasks including image segmentation, intra-operative tracking, multi-modal image alignme…
Privacy-preserving Federated Brain Tumour Segmentation
Wenqi Li, Fausto Milletarì, Daguang Xu +8
Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning al…
Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets
Reuben Dorent, Wenqi Li, Jinendra Ekanayake +2
Brain tissue segmentation from multimodal MRI is a key building block of many neuroscience analysis pipelines. It could also play an important role in many clinical imaging scenari…
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss
Guotai Wang, Jonathan Shapey, Wenqi Li +7
Automatic segmentation of vestibular schwannoma (VS) tumors from magnetic resonance imaging (MRI) would facilitate efficient and accurate volume measurement to guide patient manage…
2017 Robotic Instrument Segmentation Challenge
Max Allan, Alex Shvets, Thomas Kurmann +16
In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand t…
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation
Guotai Wang, Wenqi Li, Sebastien Ourselin +1
Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have…
Deep Boosted Regression for MR to CT Synthesis
Kerstin Kläser, Pawel Markiewicz, Marta Ranzini +7
Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification. However, attenuation correction…
Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration
Yipeng Hu, Marc Modat, Eli Gibson +11
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a…
Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Guotai Wang, Wenqi Li, Michael Aertsen +3
Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segm…
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Felix J. S. Bragman, Ryutaro Tanno, Zach Eaton-Rosen +6
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning a…
Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia
Luis C. Garcia-Peraza-Herrera, Martin Everson, Wenqi Li +10
In this work, we have concentrated our efforts on the interpretability of classification results coming from a fully convolutional neural network. Motivated by the classification o…
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Guotai Wang, Wenqi Li, Maria A. Zuluaga +8
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate…