Search Results for author: Pingkun Yan

Found 45 papers, 21 papers with code

Cardiovascular Disease Detection from Multi-View Chest X-rays with BI-Mamba

no code implementations28 May 2024 Zefan Yang, Jiajin Zhang, Ge Wang, Mannudeep K. Kalra, Pingkun Yan

Besides, BI-Mamba achieves promising performance compared with previous state of the art in CT, unraveling the potential of chest X-ray for CVD risk prediction.

Disease-informed Adaptation of Vision-Language Models

1 code implementation24 May 2024 Jiajin Zhang, Ge Wang, Mannudeep K. Kalra, Pingkun Yan

In medical image analysis, the expertise scarcity and the high cost of data annotation limits the development of large artificial intelligence models.

General Purpose Image Encoder DINOv2 for Medical Image Registration

no code implementations24 Feb 2024 Xinrui Song, Xuanang Xu, Pingkun Yan

In this paper, we present a training-free deformable image registration method, DINO-Reg, leveraging a general purpose image encoder DINOv2 for image feature extraction.

Image Registration Medical Image Registration

Multimodal Neurodegenerative Disease Subtyping Explained by ChatGPT

no code implementations31 Jan 2024 Diego Machado Reyes, Hanqing Chao, Juergen Hahn, Li Shen, Pingkun Yan

Thus, we propose a multimodal framework that uses early-stage indicators such as imaging, genetics and clinical assessments to classify AD patients into subtypes at early stages.

Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation

1 code implementation3 Sep 2023 Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan

To accomplish this challenging task, first, a spectral sensitivity map is introduced to characterize the generalization weaknesses of models in the frequency domain.

Unsupervised Domain Adaptation

Fact-Checking of AI-Generated Reports

no code implementations27 Jul 2023 Razi Mahmood, Ge Wang, Mannudeep Kalra, Pingkun Yan

Future generative AI approaches can use the resulting tool to validate their reports leading to a more responsible use of AI in expediting clinical workflows.

Fact Checking

Soft-tissue Driven Craniomaxillofacial Surgical Planning

no code implementations20 Jul 2023 Xi Fang, Daeseung Kim, Xuanang Xu, Tianshu Kuang, Nathan Lampen, Jungwook Lee, Hannah H. Deng, Jaime Gateno, Michael A. K. Liebschner, James J. Xia, Pingkun Yan

Our framework consists of a bony planner network that estimates the bony movements required to achieve the desired facial outcome and a facial simulator network that can simulate the possible facial changes resulting from the estimated bony movement plans.

Artificial General Intelligence for Medical Imaging

no code implementations8 Jun 2023 Xiang Li, Lu Zhang, Zihao Wu, Zhengliang Liu, Lin Zhao, Yixuan Yuan, Jun Liu, Gang Li, Dajiang Zhu, Pingkun Yan, Quanzheng Li, Wei Liu, Tianming Liu, Dinggang Shen

In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models.

Specialty-Oriented Generalist Medical AI for Chest CT Screening

1 code implementation3 Apr 2023 Chuang Niu, Qing Lyu, Christopher D. Carothers, Parisa Kaviani, Josh Tan, Pingkun Yan, Mannudeep K. Kalra, Christopher T. Whitlow, Ge Wang

Modern medical records include a vast amount of multimodal free text clinical data and imaging data from radiology, cardiology, and digital pathology.

Disease Prediction Lung Cancer Diagnosis +3

Distance Map Supervised Landmark Localization for MR-TRUS Registration

no code implementations11 Oct 2022 Xinrui Song, Xuanang Xu, Sheng Xu, Baris Turkbey, Bradford J. Wood, Thomas Sanford, Pingkun Yan

We then use the predicted landmarks to generate the affine transformation matrix, which outperforms the clinicians' manual rigid registration by a significant margin in terms of TRE.

Image Registration

Deep Learning-based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement

no code implementations4 Oct 2022 Xi Fang, Daeseung Kim, Xuanang Xu, Tianshu Kuang, Hannah H. Deng, Joshua C. Barber, Nathan Lampen, Jaime Gateno, Michael A. K. Liebschner, James J. Xia, Pingkun Yan

In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to estimate the facial appearance by transforming the bony movement to facial soft tissue through a point-to-point attentive correspondence matrix.

Computational Efficiency

Regression Metric Loss: Learning a Semantic Representation Space for Medical Images

1 code implementation12 Jul 2022 Hanqing Chao, Jiajin Zhang, Pingkun Yan

Regression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores.

Image Classification Medical Image Classification +1

Federated Multi-organ Segmentation with Inconsistent Labels

1 code implementation14 Jun 2022 Xuanang Xu, Hannah H. Deng, Jaime Gateno, Pingkun Yan

Extensive experiments on six public abdominal CT datasets show that our Fed-MENU method can effectively obtain a federated learning model using the partially labeled datasets with superior performance to other models trained by either localized or centralized learning methods.

Decoder Federated Learning +2

Federated Cross Learning for Medical Image Segmentation

1 code implementation5 Apr 2022 Xuanang Xu, Hannah H. Deng, Tianyi Chen, Tianshu Kuang, Joshua C. Barber, Daeseung Kim, Jaime Gateno, James J. Xia, Pingkun Yan

In this paper, we first conduct a theoretical analysis on the FL algorithm to reveal the problem of model aggregation during training on non-iid data.

Ensemble Learning Federated Learning +3

X-ray Dissectography Improves Lung Nodule Detection

no code implementations24 Mar 2022 Chuang Niu, Giridhar Dasegowda, Pingkun Yan, Mannudeep K. Kalra, Ge Wang

Although radiographs are the most frequently used worldwide due to their cost-effectiveness and widespread accessibility, the structural superposition along the x-ray paths often renders suspicious or concerning lung nodules difficult to detect.

Lung Nodule Detection

End-to-end Ultrasound Frame to Volume Registration

1 code implementation14 Jul 2021 Hengtao Guo, Xuanang Xu, Sheng Xu, Bradford J. Wood, Pingkun Yan

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.

Cross-modal Attention for MRI and Ultrasound Volume Registration

1 code implementation9 Jul 2021 Xinrui Song, Hengtao Guo, Xuanang Xu, Hanqing Chao, Sheng Xu, Baris Turkbey, Bradford J. Wood, Ge Wang, Pingkun Yan

In the past few years, convolutional neural networks (CNNs) have been proved powerful in extracting image features crucial for image registration.

Image Registration

Task-Oriented Low-Dose CT Image Denoising

1 code implementation25 Mar 2021 Jiajin Zhang, Hanqing Chao, Xuanang Xu, Chuang Niu, Ge Wang, Pingkun Yan

The extensive use of medical CT has raised a public concern over the radiation dose to the patient.

Image Denoising

Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review

no code implementations3 Mar 2021 Erim Yanik, Xavier Intes, Uwe Kruger, Pingkun Yan, David Miller, Brian Van Voorst, Basiel Makled, Jack Norfleet, Suvranu De

Here, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically survey the literature on the use of Deep Neural Networks for automated and objective surgical skill assessment, with a focus on kinematic data as putative markers of surgical competency.

Noise Entangled GAN For Low-Dose CT Simulation

no code implementations18 Feb 2021 Chuang Niu, Ge Wang, Pingkun Yan, Juergen Hahn, Youfang Lai, Xun Jia, Arjun Krishna, Klaus Mueller, Andreu Badal, KyleJ. Myers, Rongping Zeng

We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image.

Computed Tomography (CT)

Robustified Domain Adaptation

no code implementations18 Nov 2020 Jiajin Zhang, Hanqing Chao, Pingkun Yan

Unsupervised domain adaptation (UDA) is widely used to transfer knowledge from a labeled source domain to an unlabeled target domain with different data distribution.

Adversarial Robustness Unsupervised Domain Adaptation

Transducer Adaptive Ultrasound Volume Reconstruction

no code implementations17 Nov 2020 Hengtao Guo, Sheng Xu, Bradford J. Wood, Pingkun Yan

However, such algorithms are specific to particular transducers and scanning trajectories associated with the training data, which may not be generalized to other image acquisition settings.

Domain Adaptation

Stabilizing Deep Tomographic Reconstruction

no code implementations4 Aug 2020 Weiwen Wu, Dianlin Hu, Wenxiang Cong, Hongming Shan, Shao-Yu Wang, Chuang Niu, Pingkun Yan, Hengyong Yu, Varut Vardhanabhuti, Ge Wang

ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement.

Adversarial Attack Computed Tomography (CT) +1

Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning

1 code implementation13 Jun 2020 Hengtao Guo, Sheng Xu, Bradford Wood, Pingkun Yan

Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information.

Self-supervised Training of Graph Convolutional Networks

1 code implementation3 Jun 2020 Qikui Zhu, Bo Du, Pingkun Yan

Furthermore, the adjacency matrix is usually pre-defined and stationary, which makes the data augmentation strategies cannot be employed on the constructed graph structures data to augment the amount of training data.

Data Augmentation Self-Supervised Learning

Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction

1 code implementation1 Jan 2020 Xi Fang, Pingkun Yan

Shortage of fully annotated datasets has been a limiting factor in developing deep learning based image segmentation algorithms and the problem becomes more pronounced in multi-organ segmentation.

Image Segmentation Organ Segmentation +3

OASIS: One-pass aligned Atlas Set for Image Segmentation

no code implementations5 Dec 2019 Qikui Zhu, Bo Du, Pingkun Yan

Furthermore, instead of using image based similarity for label fusion, which can be distracted by the large background areas, we propose a novel strategy to compute the label similarity based weights for label fusion.

Image Registration Image Segmentation +3

Multi-hop Convolutions on Weighted Graphs

1 code implementation12 Nov 2019 Qikui Zhu, Bo Du, Pingkun Yan

To address the above weaknesses, in this paper, we propose a new method of multi-hop convolutional network on weighted graphs.

Unified Multi-scale Feature Abstraction for Medical Image Segmentation

no code implementations24 Oct 2019 Xi Fang, Bo Du, Sheng Xu, Bradford J. Wood, Pingkun Yan

Automatic medical image segmentation, an essential component of medical image analysis, plays an importantrole in computer-aided diagnosis.

Decoder Image Segmentation +3

Deep Learning in Medical Image Registration: A Survey

no code implementations5 Mar 2019 Grant Haskins, Uwe Kruger, Pingkun Yan

This survey, therefore, outlines the evolution of deep learning based medical image registration in the context of both research challenges and relevant innovations in the past few years.

Image Registration Medical Image Registration

Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

1 code implementation21 Feb 2019 Qikui Zhu, Bo Du, Pingkun Yan

To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss (BWL) is proposed.

Image Segmentation Medical Image Segmentation +2

Knowledge-based Analysis for Mortality Prediction from CT Images

1 code implementation20 Feb 2019 Hengtao Guo, Uwe Kruger, Ge Wang, Mannudeep K. Kalra, Pingkun Yan

Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality.

Clinical Knowledge Lung Cancer Diagnosis +1

On a Sparse Shortcut Topology of Artificial Neural Networks

1 code implementation22 Nov 2018 Fenglei Fan, Dayang Wang, Hengtao Guo, Qikui Zhu, Pingkun Yan, Ge Wang, Hengyong Yu

In this paper, we investigate the expressivity and generalizability of a novel sparse shortcut topology.

Hybrid deep neural networks for all-cause Mortality Prediction from LDCT Images

no code implementations19 Oct 2018 Pingkun Yan, Hengtao Guo, Ge Wang, Ruben De Man, Mannudeep K. Kalra

In this paper, we propose a deep learning based method, which takes both chest LDCT image patches and coronary artery calcification risk scores as input, for direct prediction of mortality risk of lung cancer subjects.

Lung Cancer Diagnosis Mortality Prediction

Learning Deep Similarity Metric for 3D MR-TRUS Registration

no code implementations12 Jun 2018 Grant Haskins, Jochen Kruecker, Uwe Kruger, Sheng Xu, Peter A. Pinto, Brad J. Wood, Pingkun Yan

Conclusion: A similarity metric that is learned using a deep neural network can be used to assess the quality of any given image registration and can be used in conjunction with the aforementioned optimization framework to perform automatic registration that is robust to poor initialization.

Image Registration

Adversarial Image Registration with Application for MR and TRUS Image Fusion

no code implementations30 Apr 2018 Pingkun Yan, Sheng Xu, Ardeshir R. Rastinehad, Brad J. Wood

Robust and accurate alignment of multimodal medical images is a very challenging task, which however is very useful for many clinical applications.

Image Registration

Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

9 code implementations3 Aug 2017 Qingsong Yang, Pingkun Yan, Yanbo Zhang, Hengyong Yu, Yongyi Shi, Xuanqin Mou, Mannudeep K. Kalra, Ge Wang

In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity.

Generative Adversarial Network Image Denoising

Deeply-Supervised CNN for Prostate Segmentation

no code implementations22 Mar 2017 Qikui Zhu, Bo Du, Baris Turkbey, Peter L . Choyke, Pingkun Yan

Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion.


CT Image Denoising with Perceptive Deep Neural Networks

no code implementations22 Feb 2017 Qingsong Yang, Pingkun Yan, Mannudeep K. Kalra, Ge Wang

Reduction of radiation dose associated with CT can increase noise and artifacts, which can adversely affect diagnostic confidence.

Image Denoising

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