Search Results for author: Ronald M. Summers

Found 65 papers, 14 papers with code

A Graph-theoretic Algorithm for Small Bowel Path Tracking in CT Scans

no code implementations1 Oct 2021 Seung Yeon Shin, SungWon Lee, Ronald M. Summers

It is formulated as finding the minimum cost path between given start and end nodes on a graph that is constructed based on the bowel wall detection.

Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation

no code implementations6 Jul 2021 Seung Yeon Shin, SungWon Lee, Ronald M. Summers

We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement.

Unsupervised Domain Adaptation

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

no code implementations2 Aug 2020 S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers

In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.

E$^2$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans

no code implementations19 Jul 2020 Youbao Tang, Yu-Xing Tang, Yingying Zhu, Jing Xiao, Ronald M. Summers

We introduce an edge prediction module in E$^2$Net and design an edge distance map between liver and tumor boundaries, which is used as an extra supervision signal to train the edge enhanced network.

Liver Segmentation Tumor Segmentation

Deep Small Bowel Segmentation with Cylindrical Topological Constraints

no code implementations16 Jul 2020 Seung Yeon Shin, Sung-Won Lee, Daniel C. Elton, James L. Gulley, Ronald M. Summers

Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation.

Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model

no code implementations14 Jul 2020 Yingying Zhu, You-Bao Tang, Yu-Xing Tang, Daniel C. Elton, Sung-Won Lee, Perry J. Pickhardt, Ronald M. Summers

We expect the utility of our framework will extend to other problems beyond segmentation due to the improved quality of the generated images and enhanced ability to preserve small structures.

Image-to-Image Translation Pancreas Segmentation +1

COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature

1 code implementation11 Jun 2020 Yifan Peng, Yu-Xing Tang, Sung-Won Lee, Yingying Zhu, Ronald M. Summers, Zhiyong Lu

(1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR.

Anomaly Detection Computed Tomography (CT) +1

Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Detection

no code implementations22 May 2020 Yingying Zhu, Daniel C. Elton, SungWon Lee, Perry J. Pickhardt, Ronald M. Summers

In medical imaging applications, preserving small structures is important since these structures can carry information which is highly relevant for disease diagnosis.

Image Reconstruction Object Detection +1

The Future of Digital Health with Federated Learning

no code implementations18 Mar 2020 Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett Landman, Klaus Maier-Hein, Sebastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems.

Federated Learning

Bone Suppression on Chest Radiographs With Adversarial Learning

no code implementations8 Feb 2020 Jia Liang, Yu-Xing Tang, You-Bao Tang, Jing Xiao, Ronald M. Summers

Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials, namely soft tissues, and osseous structures, to better characterize a wide variety of thoracic pathology and potentially improve diagnosis in posteroanterior (PA) chest radiographs.

Image-to-Image Translation SSIM +1

Accurately identifying vertebral levels in large datasets

no code implementations28 Jan 2020 Daniel C. Elton, Veit Sandfort, Perry J. Pickhardt, Ronald M. Summers

We next developed an algorithm which performs iterative instance segmentation and classification of the entire spine with a 3D U-Net.

Instance Segmentation Semantic Segmentation

Weakly Supervised Lesion Co-segmentation on CT Scans

no code implementations24 Jan 2020 Vatsal Agarwal, You-Bao Tang, Jing Xiao, Ronald M. Summers

In this work, we propose a weakly-supervised co-segmentation model that first generates pseudo-masks from the RECIST slices and uses these as training labels for an attention-based convolutional neural network capable of segmenting common lesions from a pair of CT scans.

Lesion Segmentation

Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation

no code implementations23 Jan 2020 Vatsal Agarwal, You-Bao Tang, Jing Xiao, Ronald M. Summers

Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth.

Computed Tomography (CT) Lesion Segmentation

$Σ$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction

1 code implementation18 Dec 2019 Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Daniel Rueckert

Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a $\Sigma$-net, for accelerated parallel MR image reconstruction using deep learning.

Image Enhancement Image Reconstruction +1

$Σ$-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction

1 code implementation11 Dec 2019 Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Kerstin Hammernik

We explore an ensembled $\Sigma$-net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps.

Image Reconstruction SSIM

MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

12 code implementations12 Aug 2019 Ke Yan, You-Bao Tang, Yifan Peng, Veit Sandfort, Mohammadhadi Bagheri, Zhiyong Lu, Ronald M. Summers

When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report.

Computed Tomography (CT)

A self-attention based deep learning method for lesion attribute detection from CT reports

no code implementations30 Apr 2019 Yifan Peng, Ke Yan, Veit Sandfort, Ronald M. Summers, Zhiyong Lu

In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity.

XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation

2 code implementations19 Apr 2019 Youbao Tang, Yu-Xing Tang, Jing Xiao, Ronald M. Summers

To reduce the manual annotation burden and to train a robust lung segmentor that can be adapted to pathological lungs with hazy lung boundaries, an image-to-image translation module is employed to synthesize radiorealistic abnormal CXRs from the source of normal ones for data augmentation.

Data Augmentation Image-to-Image Translation +1

Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier

no code implementations5 Mar 2019 Yu-Xing Tang, You-Bao Tang, Mei Han, Jing Xiao, Ronald M. Summers

Given a chest X-ray image in the testing phase, if it is normal, the learned architecture can well model and reconstruct the content; if it is abnormal, since the content is unseen in the training phase, the model would perform poorly in its reconstruction.

One-class classifier

Fine-grained lesion annotation in CT images with knowledge mined from radiology reports

no code implementations4 Mar 2019 Ke Yan, Yifan Peng, Zhiyong Lu, Ronald M. Summers

To address this problem, we define a set of 145 labels based on RadLex to describe a large variety of lesions in the DeepLesion dataset.

Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data

no code implementations23 Feb 2019 Ling Zhang, Le Lu, Xiaosong Wang, Robert M. Zhu, Mohammadhadi Bagheri, Ronald M. Summers, Jianhua Yao

Results validate that the ST-ConvLSTM produces a Dice score of 83. 2%+-5. 1% and a RVD of 11. 2%+-10. 8%, both significantly outperforming (p<0. 05) other compared methods of linear model, ConvLSTM, and generative adversarial network (GAN) under the metric of predicting future tumor volumes.

Left Ventricle Segmentation Medical Image Segmentation

ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining

1 code implementation18 Jan 2019 Youbao Tang, Ke Yan, Yu-Xing Tang, Jiamin Liu, Jing Xiao, Ronald M. Summers

To address this problem, this work constructs a pseudo mask for each lesion region that can be considered as a surrogate of the real mask, based on which the Mask R-CNN is employed for lesion detection.

Computed Tomography (CT)

Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs

no code implementations19 Jul 2018 Yu-Xing Tang, Xiaosong Wang, Adam P. Harrison, Le Lu, Jing Xiao, Ronald M. Summers

In addition, highly confident samples (measured by classification probabilities) and their corresponding class-conditional heatmaps (generated by the CNN) are extracted and further fed into the AGCL framework to guide the learning of more distinctive convolutional features in the next iteration.

Classification Curriculum Learning +2

CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement

no code implementations18 Jul 2018 Youbao Tang, Jinzheng Cai, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers

The first GAN reduces the noise in the CT image and the second GAN generates a higher resolution image with enhanced boundaries and high contrast.

Computed Tomography (CT) Image Enhancement +2

Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks

no code implementations25 Jun 2018 Youbao Tang, Adam P. Harrison, Mohammadhadi Bagheri, Jing Xiao, Ronald M. Summers

Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients.

Multi-Task Learning

Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST

no code implementations25 Jan 2018 Jinzheng Cai, You-Bao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers

Toward this end, we introduce a convolutional neural network based weakly supervised self-paced segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) adapt to segment the whole volume slice by slice to finally obtain a volumetric segmentation.

Lesion Segmentation Super-Resolution

Self-Learning to Detect and Segment Cysts in Lung CT Images without Manual Annotation

no code implementations25 Jan 2018 Ling Zhang, Vissagan Gopalakrishnan, Le Lu, Ronald M. Summers, Joel Moss, Jianhua Yao

In recent years, deep neural networks achieve impressive performances on many medical image segmentation tasks by supervised learning on large manually annotated data.

Medical Image Segmentation

DeepPap: Deep Convolutional Networks for Cervical Cell Classification

no code implementations25 Jan 2018 Ling Zhang, Le Lu, Isabella Nogues, Ronald M. Summers, Shaoxiong Liu, Jianhua Yao

However, the success of most traditional classification methods relies on the presence of accurate cell segmentations.

Classification General Classification

Convolutional Invasion and Expansion Networks for Tumor Growth Prediction

no code implementations25 Jan 2018 Ling Zhang, Le Lu, Ronald M. Summers, Electron Kebebew, Jianhua Yao

Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics.

Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks

2 code implementations12 Jul 2017 Ke Yan, Le Lu, Ronald M. Summers

In this paper, we propose a convolutional neural network (CNN) based Unsupervised Body part Regression (UBR) algorithm to address this problem.

Anomaly Detection

Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images

no code implementations12 Jun 2017 Adam P. Harrison, Ziyue Xu, Kevin George, Le Lu, Ronald M. Summers, Daniel J. Mollura

Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape.

Personalized Pancreatic Tumor Growth Prediction via Group Learning

no code implementations1 Jun 2017 Ling Zhang, Le Lu, Ronald M. Summers, Electron Kebebew, Jianhua Yao

Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor.

Feature Selection

Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition

no code implementations23 Jan 2017 Xiaosong Wang, Le Lu, Hoo-chang Shin, Lauren Kim, Mohammadhadi Bagheri, Isabella Nogues, Jianhua Yao, Ronald M. Summers

The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-annotated ImageNet and PASCAL VOC datasets.

General Classification Image Categorization +2

Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling

no code implementations19 Jan 2017 Mingchen Gao, Ziyue Xu, Le Lu, Adam P. Harrison, Ronald M. Summers, Daniel J. Mollura

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.

Computed Tomography (CT) Multi-Label Learning +1

Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation

no code implementations CVPR 2016 Hoo-chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, Ronald M. Summers

Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features.

Improving Vertebra Segmentation through Joint Vertebra-Rib Atlases

no code implementations1 Feb 2016 Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers

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.

Computed Tomography (CT)

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

no code implementations29 Jan 2016 Holger R. Roth, Yinong Wang, Jianhua Yao, Le Lu, Joseph E. Burns, Ronald M. Summers

In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine.

Osteoporotic and Neoplastic Compression Fracture Classification on Longitudinal CT

no code implementations27 Jan 2016 Yinong Wang, Jianhua Yao, Joseph E. Burns, Ronald M. Summers

Classification of vertebral compression fractures (VCF) having osteoporotic or neoplastic origin is fundamental to the planning of treatment.

Classification General Classification

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

no code implementations13 Jan 2016 Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers

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

no code implementations22 Jun 2015 Holger R. Roth, Le Lu, Amal Farag, Hoo-chang Shin, Jiamin Liu, Evrim Turkbey, Ronald M. Summers

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.

Automated Pancreas Segmentation Computed Tomography (CT) +1

Interleaved Text/Image Deep Mining on a Very Large-Scale Radiology Database

no code implementations CVPR 2015 Hoo-chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers

We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's picture archiving and communication system.

A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling

no code implementations22 May 2015 Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers

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.

Pancreas Segmentation

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

no code implementations12 May 2015 Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, Ronald M. Summers

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.

Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation

no code implementations4 May 2015 Hoo-chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers

We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's Picture Archiving and Communication System.

2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers

no code implementations14 Aug 2014 Ari Seff, Le Lu, Kevin M. Cherry, Holger Roth, Jiamin Liu, Shijun Wang, Joanne Hoffman, Evrim B. Turkbey, Ronald M. Summers

In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue.

2D Object Detection

A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans

no code implementations31 Jul 2014 Amal Farag, Le Lu, Evrim Turkbey, Jiamin Liu, Ronald M. Summers

Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis.

Computed Tomography (CT) Pancreas Segmentation

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

no code implementations22 Jul 2014 Holger R. Roth, Jianhua Yao, Le Lu, James Stieger, Joseph E. Burns, Ronald M. Summers

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.

Computed Tomography (CT)

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