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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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03/04/2023
Federated Virtual Learning on Heterogeneous Data with Local-global Distillation
Despite Federated Learning (FL)'s trend for learning machine learning mo...
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02/13/2023
PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees
Personalized Federated Learning (pFL) has emerged as a promising solutio...
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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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09/22/2022
Automated head and neck tumor segmentation from 3D PET/CT
Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platf...
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09/21/2022
Automated segmentation of intracranial hemorrhages from 3D CT
Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a ...
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09/20/2022
Automated ischemic stroke lesion segmentation from 3D MRI
Ischemic Stroke Lesion Segmentation challenge (ISLES 2022) offers a plat...
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09/13/2022
Warm Start Active Learning with Proxy Labels & Selection via Semi-Supervised Fine-Tuning
Which volume to annotate next is a challenging problem in building medic...
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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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01/04/2022
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Semantic segmentation of brain tumors is a fundamental medical image ana...
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12/20/2021
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet
Semantic segmentation of 3D medical images is a challenging task due to ...
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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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11/15/2021
T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging
Lesion segmentation in medical imaging has been an important topic in cl...
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11/01/2021
Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging
Multiple instance learning (MIL) is a key algorithm for classification o...
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10/06/2021
Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports
Localization and characterization of diseases like pneumonia are primary...
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08/19/2021
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Federated learning (FL) for medical image segmentation becomes more chal...
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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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07/12/2021
The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation
Deep learning models for medical image segmentation are primarily data-d...
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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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03/30/2021
Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays
Pre-trained models, e.g., from ImageNet, have proven to be effective in ...
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03/29/2021
DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
Recently, neural architecture search (NAS) has been applied to automatic...
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03/25/2021
Test-Time Training for Deformable Multi-Scale Image Registration
Registration is a fundamental task in medical robotics and is often a cr...
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03/18/2021
UNETR: Transformers for 3D Medical Image Segmentation
Fully Convolutional Neural Networks (FCNNs) with contracting and expansi...
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01/07/2021
Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation
Active learning is a unique abstraction of machine learning techniques w...
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11/28/2020
Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19
Previous work established skip-gram word2vec models could be used to min...
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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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09/28/2020
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
The performance of deep learning-based methods strongly relies on the nu...
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09/25/2020
Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
The training of deep learning models typically requires extensive data, ...
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09/25/2020
Going to Extremes: Weakly Supervised Medical Image Segmentation
Medical image annotation is a major hurdle for developing precise and ro...
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09/22/2020
Learning Image Labels On-the-fly for Training Robust Classification Models
Current deep learning paradigms largely benefit from the tremendous amou...
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09/11/2020
Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
CXRs are a crucial and extraordinarily common diagnostic tool, leading t...
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07/31/2020
Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
Detecting clinically relevant objects in medical images is a challenge d...
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07/10/2020
Multi-Domain Image Completion for Random Missing Input Data
Multi-domain data are widely leveraged in vision applications taking adv...
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06/28/2020
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Although having achieved great success in medical image segmentation, de...
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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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06/10/2020
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
Deep neural network (DNN) based approaches have been widely investigated...
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05/29/2020
Enhancing Foreground Boundaries for Medical Image Segmentation
Object segmentation plays an important role in the modern medical image ...
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02/19/2020
When Radiology Report Generation Meets Knowledge Graph
Automatic radiology report generation has been an attracting research pr...
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01/24/2020
VerSe: A Vertebrae Labelling and Segmentation Benchmark
In this paper we report the challenge set-up and results of the Large Sc...
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12/20/2019
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
3D convolution neural networks (CNN) have been proved very successful in...
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10/15/2019
End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation
Automatic segmentation of abdomen organs using medical imaging has many ...
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10/04/2019
