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
43 papers · 1 filter
Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine
Qiao Jin, Fangyuan Chen, Yiliang Zhou +15
Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily…
Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study
Benjamin Hou, Tejas Sudharshan Mathai, Jianfei Liu +2
Purpose: Body composition measurements from routine abdominal CT can yield personalized risk assessments for asymptomatic and diseased patients. In particular, attenuation and volu…
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Gregory Holste, Yiliang Zhou, Song Wang +22
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more rela…
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?
Gregory Holste, Ziyu Jiang, Ajay Jaiswal +12
Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. Howe…
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering
Xinyue Hu, Lin Gu, Qiyuan An +6
To contribute to automating the medical vision-language model, we propose a novel Chest-Xray Difference Visual Question Answering (VQA) task. Given a pair of main and reference ima…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Graph-Based Small Bowel Path Tracking with Cylindrical Constraints
Seung Yeon Shin, Sungwon Lee, Ronald M. Summers
We present a new graph-based method for small bowel path tracking based on cylindrical constraints. A distinctive characteristic of the small bowel compared to other organs is the…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Transducer Adaptive Ultrasound Volume Reconstruction
Hengtao Guo, Sheng Xu, Bradford J. Wood +1
Reconstructed 3D ultrasound volume provides more context information compared to a sequence of 2D scanning frames, which is desirable for various clinical applications such as ultr…
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos +6
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propell…
Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
Leo K. Tam, Xiaosong Wang, Evrim Turkbey +3
Detecting clinically relevant objects in medical images is a challenge despite large datasets due to the lack of detailed labels. To address the label issue, we utilize the scene-l…
Multi-Domain Image Completion for Random Missing Input Data
Liyue Shen, Wentao Zhu, Xiaosong Wang +9
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-par…
Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning
Hengtao Guo, Sheng Xu, Bradford Wood +1
Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information. Current methods for 3D v…
Fine-grained lesion annotation in CT images with knowledge mined from radiology reports
Ke Yan, Yifan Peng, Zhiyong Lu +1
In radiologists' routine work, one major task is to read a medical image, e.g., a CT scan, find significant lesions, and write sentences in the radiology report to describe them. I…
Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury
Samuel Remedios, Snehashis Roy, Justin Blaber +6
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient…
Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier
Yuxing Tang, Youbao Tang, Mei Han +2
Being one of the most common diagnostic imaging tests, chest radiography requires timely reporting of potential findings in the images. In this paper, we propose an end-to-end arch…
Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data
Ling Zhang, Le Lu, Xiaosong Wang +4
Prognostic tumor growth modeling via volumetric medical imaging observations can potentially lead to better outcomes of tumor treatment and surgical planning. Recent advances of co…
ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining
Youbao Tang, Ke Yan, Yuxing Tang +3
Automatic lesion detection from computed tomography (CT) scans is an important task in medical imaging analysis. It is still very challenging due to similar appearances (e.g. inten…
3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection
Ke Yan, Mohammadhadi Bagheri, Ronald M. Summers
Detecting lesions from computed tomography (CT) scans is an important but difficult problem because non-lesions and true lesions can appear similar. 3D context is known to be helpf…
Synthesizing CT from Ultrashort Echo-Time MR Images via Convolutional Neural Networks
Snehashis Roy, John A. Butman, Dzung L. Pham
With the increasing popularity of PET-MR scanners in clinical applications, synthesis of CT images from MR has been an important research topic. Accurate PET image reconstruction r…
Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs
Yuxing Tang, Xiaosong Wang, Adam P. Harrison +3
In this work, we exploit the task of joint classification and weakly supervised localization of thoracic diseases from chest radiographs, with only image-level disease labels coupl…
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
Youbao Tang, Jinzheng Cai, Le Lu +5
Automated lesion segmentation from computed tomography (CT) is an important and challenging task in medical image analysis. While many advancements have been made, there is room fo…
Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks
Youbao Tang, Adam P. Harrison, Mohammadhadi Bagheri +2
Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients. As such, RECIST annotations m…
Adversarial Image Registration with Application for MR and TRUS Image Fusion
Pingkun Yan, Sheng Xu, Ardeshir R. Rastinehad +1
Robust and accurate alignment of multimodal medical images is a very challenging task, which however is very useful for many clinical applications. For example, magnetic resonance…