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Found 47 papers, 9 papers with code
Empowering Federated Learning for Massive Models with NVIDIA FLARE
In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge.
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models
However, applying FL to finetune PLMs is hampered by challenges, including restricted model parameter access, high computational requirements, and communication overheads.
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data
Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data.
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks.
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.
Warm Start Active Learning with Proxy Labels \& Selection via Semi-Supervised Fine-Tuning
Which volume to annotate next is a challenging problem in building medical imaging datasets for deep learning.
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.
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images
MONAI Label allows researchers to make incremental improvements to their AI-based annotation application by making them available to other researchers and clinicians alike.
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.
Do Gradient Inversion Attacks Make Federated Learning Unsafe?
Federated learning (FL) allows the collaborative training of AI models without needing to share raw data.
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.
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
Building robust deep learning-based models requires diverse training data, ideally from several sources.
Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation
The primary advantage being that active learning frameworks select data points that can accelerate the learning process of a model and can reduce the amount of data needed to achieve full accuracy as compared to a model trained on a randomly acquired data set.
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.
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
The performance of deep learning-based methods strongly relies on the number of datasets used for training.
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.
Federated Learning for Breast Density Classification: A Real-World Implementation
Building robust deep learning-based models requires large quantities of diverse training data.
Colon Shape Estimation Method for Colonoscope Tracking using Recurrent Neural Networks
We propose a colon deformation estimation method using RNN and obtain the colonoscope shape from electromagnetic sensors during its insertion into the colon.
Colonoscope tracking method based on shape estimation network
We utilize the shape estimation network (SEN), which estimates deformed colon shape during colonoscope insertions.
Precise Estimation of Renal Vascular Dominant Regions Using Spatially Aware Fully Convolutional Networks, Tensor-Cut and Voronoi Diagrams
Then we generate a Voronoi diagram to estimate the renal vascular dominant regions based on the segmented kidney and renal arteries.
3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation
We estimate the position and the size of the pancreas (localized) from global features by regression forests.
A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation
Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images.
Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning
This paper presents a novel unsupervised segmentation method for 3D medical images.
Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-means
In this paper, we propose a unified approach to unsupervised representation learning and clustering for pathology image segmentation.
Deep learning and its application to medical image segmentation
However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision.
An application of cascaded 3D fully convolutional networks for medical image segmentation
In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models.
Ranked #2 on
3D Medical Imaging Segmentation
on TCIA Pancreas-CT
On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks
Deep learning-based methods achieved impressive results for the segmentation of medical images.
Hierarchical 3D fully convolutional networks for multi-organ segmentation
In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of seven abdominal structures (artery, vein, liver, spleen, stomach, gallbladder, and pancreas) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training organ-specific models.
Comparison of the Deep-Learning-Based Automated Segmentation Methods for the Head Sectioned Images of the Virtual Korean Human Project
This paper presents an end-to-end pixelwise fully automated segmentation of the head sectioned images of the Visible Korean Human (VKH) project based on Deep Convolutional Neural Networks (DCNNs).
Multi-scale Image Fusion Between Pre-operative Clinical CT and X-ray Microtomography of Lung Pathology
The alignment of clinical CT with $\mu$CT will allow further registration with even finer resolutions of $\mu$CT (up to 10 $\mu$m resolution) and ultimately with histopathological microscopy images for further macro to micro image fusion that can aid medical image analysis.
Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentation
Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis.
Ranked #1 on
3D Medical Imaging Segmentation
on TCIA Pancreas-CT
Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation
Accurate automatic organ segmentation is an important yet challenging problem for medical image analysis.
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.
Improving Vertebra Segmentation through Joint Vertebra-Rib Atlases
The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92. 5 $\pm$ 3. 1% to 93. 8 $\pm$ 2. 1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0. 75 $\pm$ 0. 60mm and 8. 63 $\pm$ 4. 44mm to 0. 30 $\pm$ 0. 27mm and 3. 65 $\pm$ 2. 87mm, respectively.
Deep convolutional networks for automated detection of posterior-element fractures on spine CT
In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine.
Multi-Atlas Segmentation with Joint Label Fusion of Osteoporotic Vertebral Compression Fractures on CT
The precise and accurate segmentation of the vertebral column is essential in the diagnosis and treatment of various orthopedic, neurological, and oncological traumas and pathologies.
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i. e. superpixels.
A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling
We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels.
Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation
By leveraging existing CAD systems, coordinates of regions or volumes of interest (ROI or VOI) for lesion candidates are generated in this step and function as input for a second tier, which is our focus in this study.
Anatomy-specific classification of medical images using deep convolutional nets
We show that a data augmentation approach can help to enrich the data set and improve classification performance.
Deep convolutional networks for pancreas segmentation in CT imaging
We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing).
Computer-assisted polyp matching between optical colonoscopy and CT colonography: a phantom study
Furthermore, we evaluate the matching of the reconstructed polyp from OC with other colonic endoluminal surface structures such as haustral folds and show that there is a minimum at the correct polyp from CTC.
Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications
In testing, the CNN is employed to assign individual probabilities for a new set of N random views that are averaged at each ROI to compute a final per-candidate classification probability.
