Search Results for author:
Found 44 papers, 9 papers with code
Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training
We curate a large-scale dataset to enable pre-training of 3D medical radiology images (MRI and CT).
A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing
Language models (LMs) like BERT and GPT have revolutionized natural language processing (NLP).
Fair Federated Medical Image Segmentation via Client Contribution Estimation
Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness).
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
We propose a practical vertical federated learning (VFL) framework called \textbf{one-shot VFL} that can solve the communication bottleneck and the problem of limited overlapping samples simultaneously based on semi-supervised learning.
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
NVIDIA FLARE: Federated Learning from Simulation to Real-World
Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data.
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 decentralized manner.
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 outstanding modeling capabilities, in particular for capturing long-range information, and scalability to dataset and model sizes which has led to state-of-the-art performance in various computer vision and medical image analysis tasks.
Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation
In this work, we propose a novel training framework FedSM to avoid the client drift issue and successfully close the generalization gap compared with the centralized training for medical image segmentation tasks for the first time.
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing.
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 clinical research.
Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging
Multiple instance learning (MIL) is a key algorithm for classification of whole slide images (WSI).
Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports
Both the classification and localization are trained in conjunction and once trained, the model can be utilized for both the localization and characterization of pneumonia using only the input image.
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
Building robust deep learning-based models requires diverse training data, ideally from several sources.
FedGL: Federated Graph Learning Framework with Global Self-Supervision
Moreover, the global self-supervision enables the information of each client to flow and share in a privacy-preserving manner, thus alleviating the heterogeneity and utilizing the complementarity of graph data among different clients.
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels.
Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays
In this work, we introduce an image-text pre-training framework that can learn from these raw data with mixed data inputs, i. e., paired image-text data, a mixture of paired and unpaired data.
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results.
Going to Extremes: Weakly Supervised Medical Image Segmentation
Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation.
Learning Image Labels On-the-fly for Training Robust Classification Models
We apply the attention-on-label scheme on the classification task of a synthetic noisy CIFAR-10 dataset to prove the concept, and then demonstrate superior results (3-5% increase on average in multiple disease classification AUCs) on the chest x-ray images from a hospital-scale dataset (MIMIC-CXR) and hand-labeled dataset (OpenI) in comparison to regular training paradigms.
ScribbleBox: Interactive Annotation Framework for Video Object Segmentation
Our approach tolerates a modest amount of noise in the box placements, thus typically only a few clicks are needed to annotate tracked boxes to a sufficient accuracy.
GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI
This paper proposes an alternative approach to the aforementioned, where AD diagnosis is incorporated in the GAN training objective to achieve the best AD classification performance.
LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
In this work, we introduce Large deep 3D ConvNets with Automated Model Parallelism (LAMP) and investigate the impact of both input's and deep 3D ConvNets' size on segmentation accuracy.
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
For example, fully convolutional neural networks (FCN) achieve the state-of-the-art performance in several applications of 2D/3D medical image segmentation.
Enhancing Foreground Boundaries for Medical Image Segmentation
Object segmentation plays an important role in the modern medical image analysis, which benefits clinical study, disease diagnosis, and surgery planning.
Capsules for Biomedical Image Segmentation
The proposed convolutional-deconvolutional capsule network, SegCaps, shows state-of-the-art results while using a fraction of the parameters of popular segmentation networks.
When Radiology Report Generation Meets Knowledge Graph
In addition, we proposed a new evaluation metric for radiology image reporting with the assistance of the same composed graph.
Training Models 20X Faster in Medical Image Analysis
Analyzing high-dimensional medical images (2D/3D/4D CT, MRI, histopathological images, etc.)
Correlation via Synthesis: End-to-end Image Generation and Radiogenomic Learning Based on Generative Adversarial Network
In this work, we investigate the potential of an end-to-end method fusing gene code with image features to generate synthetic pathology image and learn radiogenomic map simultaneously.
NeurReg: Neural Registration and Its Application to Image Segmentation
Furthermore, we design three segmentation frameworks based on the proposed registration framework: 1) atlas-based segmentation, 2) joint learning of both segmentation and registration tasks, and 3) multi-task learning with atlas-based segmentation as an intermediate feature.
Cardiac Segmentation of LGE MRI with Noisy Labels
In the first step, we register a small set of five LGE cardiac magnetic resonance (CMR) images with ground truth labels to a set of 40 target LGE CMR images without annotation.
Weakly supervised segmentation from extreme points
Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images.
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 useful for noninvasively identifying molecular properties of a particular type of disease.
When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
We rethink data augmentation for medical 3D images and propose a deep stacked transformations (DST) approach for domain generalization.
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Segmentation methods had to be containerized and submitted to the challenge organizers.
Interactive segmentation of medical images through fully convolutional neural networks
Semi-automated approaches keep users in control of the results by providing means for interaction, but the main challenge is to offer a good trade-off between precision and required interaction.
CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation
Qualitative results demonstrate the effectiveness of our method compared to the state-of-art.
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) are of great importance in studying and understanding various neurological and geriatric disorders.
Pathological Pulmonary Lobe Segmentation from CT Images using Progressive Holistically Nested Neural Networks and Random Walker
We are the first to apply deep learning to PPLS.
Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images
Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape.
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling
Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal.
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
Both sets of features were combined to estimate the nodule malignancy using a random forest classifier.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Another effective method is transfer learning, i. e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks.
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies.
