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07/31/2023
Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training
Harnessing the power of pre-training on large-scale datasets like ImageN...
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03/28/2023
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
Federated learning is a popular collaborative learning approach that ena...
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11/04/2022
MONAI: An open-source framework for deep learning in healthcare
Artificial Intelligence (AI) is having a tremendous impact across most a...
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10/24/2022
NVIDIA FLARE: Federated Learning from Simulation to Real-World
Federated learning (FL) enables the building of robust and generalizable...
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08/22/2022
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation
Split learning (SL) has been proposed to train deep learning models in a...
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04/01/2022
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation
Vision Transformers (ViT)s have recently become popular due to their out...
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03/23/2022
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images
The lack of annotated datasets is a major challenge in training new task...
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03/22/2022
GradViT: Gradient Inversion of Vision Transformers
In this work we demonstrate the vulnerability of vision transformers (Vi...
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03/18/2022
Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation
Cross-silo federated learning (FL) has attracted much attention in medic...
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03/12/2022
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Federated learning (FL) is a distributed machine learning technique that...
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02/14/2022
Do Gradient Inversion Attacks Make Federated Learning Unsafe?
Federated learning (FL) allows the collaborative training of AI models w...
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11/29/2021
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Vision Transformers (ViT)s have shown great performance in self-supervis...
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07/16/2021
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
Building robust deep learning-based models requires diverse training dat...
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04/20/2021
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
Federated learning (FL) enables collaborative model training while prese...
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01/05/2021
Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation
Multi-modal medical image segmentation plays an essential role in clinic...
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11/23/2020
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...
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06/22/2020
LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
Deep Learning (DL) models are becoming larger, because the increase in m...
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03/09/2020
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...
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10/04/2019
NeurReg: Neural Registration and Its Application to Image Segmentation
Registration is a fundamental task in medical image analysis which can b...
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10/02/2019
Privacy-preserving Federated Brain Tumour Segmentation
Due to medical data privacy regulations, it is often infeasible to colle...
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07/07/2019
Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets
Brain tissue segmentation from multimodal MRI is a key building block of...
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06/10/2019
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...
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02/18/2019
2017 Robotic Instrument Segmentation Challenge
In mainstream computer vision and machine learning, public datasets such...
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11/05/2018
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...
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10/18/2018
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation
Automatic brain tumor segmentation plays an important role for diagnosis...
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08/22/2018
Deep Boosted Regression for MR to CT Synthesis
Attenuation correction is an essential requirement of positron emission ...
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07/19/2018
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,...
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07/19/2018
Test-time augmentation with uncertainty estimation for deep learning-based medical image segmentation
Data augmentation has been widely used for training deep learning system...
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07/09/2018
Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration
One of the fundamental challenges in supervised learning for multimodal ...
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06/18/2018
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Multi-task neural network architectures provide a mechanism that jointly...
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05/02/2018
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...
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10/11/2017
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Convolutional neural networks (CNNs) have achieved state-of-the-art perf...
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09/11/2017
NiftyNet: a deep-learning platform for medical imaging
Medical image analysis and computer-assisted intervention problems are i...
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07/11/2017
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...
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07/06/2017
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...
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07/03/2017
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Accurate medical image segmentation is essential for diagnosis, surgical...
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07/03/2017
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...
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06/25/2017
ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools
Real-time tool segmentation from endoscopic videos is an essential part ...
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06/25/2017