3.1k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- National Institutes of HealthUS40 papers
- National Cancer InstituteUS8 papers
- Ping An (China)8 papers
- National Center for Biotechnology InformationUS7 papers
- Nvidia (United States)US7 papers
- University of PennsylvaniaUS5 papers
- Vanderbilt UniversityUS5 papers
- German Cancer Research CenterDE4 papers
- Heidelberg UniversityDE4 papers
- Imperial College LondonGB4 papers
- King's College LondonGB4 papers
- Radboud University Medical CenterNL4 papers
18 papers · 1 filter
Classification of Multi-Parametric Body MRI Series Using Deep Learning
Boah Kim, Tejas Sudharshan Mathai, Kimberly Helm +2
Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrec…
Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification
Benjamin Hou, Sung-Won Lee, Jung-Min Lee +4
Purpose: To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and ovarian…
Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification
Ricardo Bigolin Lanfredi, Pritam Mukherjee, Ronald Summers
In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. However, there is still room for improvement in la…
Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks
Kimberly Helm, Tejas Sudharshan Mathai, Boah Kim +3
Multi-parametric MRI of the body is routinely acquired for the identification of abnormalities and diagnosis of diseases. However, a standard naming convention for the MRI protocol…
Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision
Seung Yeon Shin, Thomas C. Shen, Ronald M. Summers
We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesio…
A bone suppression model ensemble to improve COVID-19 detection in chest X-rays
Sivaramakrishnan Rajaraman, Gregg Cohen, Lillian Spear +2
Chest X-ray (CXR) is a widely performed radiology examination that helps to detect abnormalities in the tissues and organs in the thoracic cavity. Detecting pulmonary abnormalities…
End-to-end Ultrasound Frame to Volume Registration
Hengtao Guo, Xuanang Xu, Sheng Xu +2
Fusing intra-operative 2D transrectal ultrasound (TRUS) image with pre-operative 3D magnetic resonance (MR) volume to guide prostate biopsy can significantly increase the yield. Ho…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
Chest X-Ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings
Sivaramakrishnan Rajaraman, Ghada Zamzmi, Les Folio +2
Chest X-rays are the most commonly performed diagnostic examination to detect cardiopulmonary abnormalities. However, the presence of bony structures such as ribs and clavicles can…
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
Dong Yang, Ziyue Xu, Wenqi Li +17
The recent outbreak of COVID-19 has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. As a complimentary tool, chest CT has been shown to be able t…
One Click Lesion RECIST Measurement and Segmentation on CT Scans
Youbao Tang, Ke Yan, Jing Xiao +1
In clinical trials, one of the radiologists' routine work is to measure tumor sizes on medical images using the RECIST criteria (Response Evaluation Criteria In Solid Tumors). Howe…
ENet: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans
Youbao Tang, Yuxing Tang, Yingying Zhu +2
Developing an effective liver and liver tumor segmentation model from CT scans is very important for the success of liver cancer diagnosis, surgical planning and cancer treatment.…
Deep Small Bowel Segmentation with Cylindrical Topological Constraints
Seung Yeon Shin, Sungwon Lee, Daniel C. Elton +2
We present a novel method for small bowel segmentation where a cylindrical topological constraint based on persistent homology is applied. To address the touching issue which could…
Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model
Yingying Zhu, Youbao Tang, Yuxing Tang +4
Current deep learning based segmentation models often generalize poorly between domains due to insufficient training data. In real-world clinical applications, cross-domain image a…
Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks
R. Robinson, Q. Dou, D. C. Castro +5
We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed wi…
COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature
Yifan Peng, Yu-Xing Tang, Sungwon Lee +3
The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collec…
Bone Suppression on Chest Radiographs With Adversarial Learning
Jia Liang, Yuxing Tang, Youbao Tang +2
Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials, namely soft tissues, and osseous structures, to better characte…
Accurately identifying vertebral levels in large datasets
Daniel C. Elton, Veit Sandfort, Perry J. Pickhardt +1
The vertebral levels of the spine provide a useful coordinate system when making measurements of plaque, muscle, fat, and bone mineral density. Correctly classifying vertebral leve…