Holger R. Roth

research

∙ 03/28/2023

Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

Federated learning is a popular collaborative learning approach that ena...

0 Jingwei Sun, et al. ∙

research

∙ 11/04/2022

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most a...

6 M. Jorge Cardoso, et al. ∙

research

∙ 10/24/2022

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Federated learning (FL) enables the building of robust and generalizable...

0 Holger R. Roth, et al. ∙

research

∙ 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...

0 Vishwesh Nath, et al. ∙

research

∙ 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...

0 Holger R. Roth, et al. ∙

research

∙ 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...

31 Andres Diaz-Pinto, et al. ∙

research

∙ 03/12/2022

Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

Federated learning (FL) is a distributed machine learning technique that...

0 Pengfei Guo, et al. ∙

research

∙ 02/14/2022

Do Gradient Inversion Attacks Make Federated Learning Unsafe?

Federated learning (FL) allows the collaborative training of AI models w...

0 Ali Hatamizadeh, et al. ∙

research

∙ 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...

0 Dong Yang, et al. ∙

research

∙ 08/19/2021

Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

Federated learning (FL) for medical image segmentation becomes more chal...

0 Chen Shen, et al. ∙

research

∙ 07/16/2021

Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures

Building robust deep learning-based models requires diverse training dat...

21 Holger R. Roth, et al. ∙

research

∙ 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...

25 Vishwesh Nath, et al. ∙

research

∙ 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...

5 Dong Yang, et al. ∙

research

∙ 09/28/2020

Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning

The performance of deep learning-based methods strongly relies on the nu...

71 Pochuan Wang, et al. ∙

research

∙ 09/25/2020

Going to Extremes: Weakly Supervised Medical Image Segmentation

Medical image annotation is a major hurdle for developing precise and ro...

39 Holger R. Roth, et al. ∙

research

∙ 04/20/2020

Colonoscope tracking method based on shape estimation network

This paper presents a colonoscope tracking method utilizing a colon shap...

9 Masahiro Oda, et al. ∙

research

∙ 08/05/2019

Precise Estimation of Renal Vascular Dominant Regions Using Spatially Aware Fully Convolutional Networks, Tensor-Cut and Voronoi Diagrams

This paper presents a new approach for precisely estimating the renal va...

3 Chenglong Wang, et al. ∙

research

∙ 06/08/2018

3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation

This paper presents a fully automated atlas-based pancreas segmentation ...

8 Masahiro Oda, et al. ∙

research

∙ 06/06/2018

A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation

Recent advances in deep learning, like 3D fully convolutional networks (...

0 Holger R. Roth, et al. ∙

research

∙ 04/11/2018

Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning

This paper presents a novel unsupervised segmentation method for 3D medi...

0 Takayasu Moriya, et al. ∙

research

∙ 04/11/2018

Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-means

This paper presents a novel method for unsupervised segmentation of path...

0 Takayasu Moriya, et al. ∙

research

∙ 03/23/2018

Deep learning and its application to medical image segmentation

One of the most common tasks in medical imaging is semantic segmentation...

0 Holger R. Roth, et al. ∙

research

∙ 03/14/2018

An application of cascaded 3D fully convolutional networks for medical image segmentation

Recent advances in 3D fully convolutional networks (FCN) have made it fe...

0 Holger R. Roth, et al. ∙

research

∙ 01/18/2018

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 segmenta...

0 Chen Shen, et al. ∙

research

∙ 03/15/2017

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...

0 Mohammad Eshghi, et al. ∙

research

∙ 02/27/2017

Multi-scale Image Fusion Between Pre-operative Clinical CT and X-ray Microtomography of Lung Pathology

Computational anatomy allows the quantitative analysis of organs in medi...

0 Holger R. Roth, et al. ∙

research

∙ 01/31/2017

Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentation

Accurate and automatic organ segmentation from 3D radiological scans is ...

0 Holger R. Roth, et al. ∙

research

∙ 06/24/2016

Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation

Accurate automatic organ segmentation is an important yet challenging pr...

0 Holger R. Roth, et al. ∙

research

∙ 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...

0 Hoo-chang Shin, et al. ∙

research

∙ 02/01/2016

Improving Vertebra Segmentation through Joint Vertebra-Rib Atlases

Accurate spine segmentation allows for improved identification and quant...

0 Yinong Wang, et al. ∙

research

∙ 01/29/2016

Deep convolutional networks for automated detection of posterior-element fractures on spine CT

Injuries of the spine, and its posterior elements in particular, are a c...

0 Holger R. Roth, et al. ∙

research

∙ 01/13/2016

Multi-Atlas Segmentation with Joint Label Fusion of Osteoporotic Vertebral Compression Fractures on CT

The precise and accurate segmentation of the vertebral column is essenti...

0 Yinong Wang, et al. ∙

research

∙ 06/22/2015

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Automatic organ segmentation is an important yet challenging problem for...

0 Holger R. Roth, et al. ∙

research

∙ 05/12/2015

Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation

Automated computer-aided detection (CADe) in medical imaging has been an...

0 Holger R. Roth, et al. ∙

research

∙ 04/15/2015

Anatomy-specific classification of medical images using deep convolutional nets

Automated classification of human anatomy is an important prerequisite f...

0 Holger R. Roth, et al. ∙

research

∙ 04/15/2015

Deep convolutional networks for pancreas segmentation in CT imaging

Automatic organ segmentation is an important prerequisite for many compu...

0 Holger R. Roth, et al. ∙

research

∙ 01/15/2015

Computer-assisted polyp matching between optical colonoscopy and CT colonography: a phantom study

Potentially precancerous polyps detected with CT colonography (CTC) need...

0 Holger R. Roth, et al. ∙

research

∙ 07/22/2014

Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications

Automated detection of sclerotic metastases (bone lesions) in Computed T...

0 Holger R. Roth, et al. ∙

research

∙ 06/06/2014

A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations

Automated Lymph Node (LN) detection is an important clinical diagnostic ...

0 Holger R. Roth, et al. ∙

Holger R. Roth

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