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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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03/18/2022
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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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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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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05/07/2021
FedGL: Federated Graph Learning Framework with Global Self-Supervision
Graph data are ubiquitous in the real world. Graph learning (GL) tries t...
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04/07/2021
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
Visual explanation methods have an important role in the prognosis of th...
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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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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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09/25/2020
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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08/22/2020
ScribbleBox: Interactive Annotation Framework for Video Object Segmentation
Manually labeling video datasets for segmentation tasks is extremely tim...
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08/10/2020
GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI
Positron Emission Tomography (PET) is now regarded as the gold standard ...
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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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04/09/2020
Capsules for Biomedical Image Segmentation
Our work expands the use of capsule networks to the task of object segme...
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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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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
Cardiac Segmentation of LGE MRI with Noisy Labels
In this work, we attempt the segmentation of cardiac structures in late ...
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10/02/2019
Weakly supervised segmentation from extreme points
Annotation of medical images has been a major bottleneck for the develop...
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07/08/2019
Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network
Radiogenomic map linking image features and gene expression profiles is ...
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06/07/2019
When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
Recent advances in deep learning for medical image segmentation demonstr...
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04/01/2019
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Quantification of cerebral white matter hyperintensities (WMH) of presum...
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03/19/2019
Interactive segmentation of medical images through fully convolutional neural networks
Image segmentation plays an essential role in medicine for both diagnost...
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06/11/2018
CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation
Data availability plays a critical role for the performance of deep lear...
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03/19/2018
White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections
Segmentation and quantification of white matter hyperintensities (WMHs) ...
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06/12/2017
Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images
Pathological lung segmentation (PLS) is an important, yet challenging, m...
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01/19/2017
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling
Accurately predicting and detecting interstitial lung disease (ILD) patt...
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09/21/2016
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
Computed tomography imaging is a standard modality for detecting and ass...
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02/10/2016
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Remarkable progress has been made in image recognition, primarily due to...
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07/11/2014