Search Results for author: S. Kevin Zhou

Found 91 papers, 22 papers with code

Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation

no code implementations11 May 2023 Ziyuan Zhao, Fangcheng Zhou, Zeng Zeng, Cuntai Guan, S. Kevin Zhou

To achieve efficient few-shot cross-modality segmentation, we propose a novel transformation-consistent meta-hallucination framework, meta-hallucinator, with the goal of learning to diversify data distributions and generate useful examples for enhancing cross-modality performance.

Cardiac Segmentation Image Segmentation +4

Unified Multi-Modal Image Synthesis for Missing Modality Imputation

no code implementations11 Apr 2023 Yue Zhang, Chengtao Peng, Qiuli Wang, Dan Song, Kaiyan Li, S. Kevin Zhou

Besides, we propose a Dynamic Feature Unification Module to integrate information from a varying number of available modalities, which enables the network to be robust to random missing modalities.

Anatomy Image Generation +1

FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising

1 code implementation2 Apr 2023 Bo Zhou, Huidong Xie, Qiong Liu, Xiongchao Chen, Xueqi Guo, Zhicheng Feng, S. Kevin Zhou, Biao Li, Axel Rominger, Kuangyu Shi, James S. Duncan, Chi Liu

While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored.

Denoising Personalized Federated Learning

Causal Image Synthesis of Brain MR in 3D

no code implementations25 Mar 2023 Yujia Li, Jiong Shi, S. Kevin Zhou

Clinical decision making requires counterfactual reasoning based on a factual medical image and thus necessitates causal image synthesis.

Decision Making Image Generation

Evidence-aware multi-modal data fusion and its application to total knee replacement prediction

no code implementations24 Mar 2023 Xinwen Liu, Jing Wang, S. Kevin Zhou, Craig Engstrom, Shekhar S. Chandra

For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch.

DuDoRNeXt: A hybrid model for dual-domain undersampled MRI reconstruction

no code implementations19 Mar 2023 Ziqi Gao, S. Kevin Zhou

Recent deep learning methods for MRI reconstruction adopt CNN or ViT as backbone, which lack in utilizing the complementary properties of CNN and ViT.

Layout Design MRI Reconstruction

GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision

no code implementations16 Mar 2023 Mingyue Zhao, Shang Zhao, Quan Quan, Li Fan, Xiaolan Qiu, Shiyuan Liu, S. Kevin Zhou

To address these problems, we contribute a new bronchial segmentation method based on Group Deep Dense Supervision (GDDS) that emphasizes fine-scale bronchioles segmentation in a simple-but-effective manner.

Lung Nodule Segmentation and Low-Confidence Region Prediction with Uncertainty-Aware Attention Mechanism

no code implementations15 Mar 2023 Han Yang, Qiuli Wang, Yue Zhang, Zhulin An, Chen Liu, Xiaohong Zhang, S. Kevin Zhou

In this paper, we propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation.

Lung Nodule Segmentation

FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification

no code implementations15 Mar 2023 Zikang Xu, Shang Zhao, Quan Quan, Qingsong Yao, S. Kevin Zhou

Deep learning is becoming increasingly ubiquitous in medical research and applications while involving sensitive information and even critical diagnosis decisions.


O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis

no code implementations11 Mar 2023 Jun Li, Kexin Li, Yafeng Zhou, S. Kevin Zhou

Therefore, it is clinically critical to introduce annotations of plaque tissue and lumen characteristics from OCT to paired CCTA scans, denoted as \textbf{the O2CTA problem} in this paper.

Distortion-Disentangled Contrastive Learning

no code implementations9 Mar 2023 Jinfeng Wang, Sifan Song, Jionglong Su, S. Kevin Zhou

The POCL method typically uses a single loss function to extract the distortion invariant representation (DIR) which describes the proximity of positive-pair representations affected by different distortions.

Contrastive Learning Disentanglement +1

Mixed-order self-paced curriculum learning for universal lesion detection

no code implementations9 Feb 2023 Han Li, Hu Han, S. Kevin Zhou

Most SCL methods commonly adopt a loss-based strategy of estimating data difficulty and deweighting the `hard' samples in the early training stage.

Lesion Detection

Multi-site Organ Segmentation with Federated Partial Supervision and Site Adaptation

no code implementations8 Feb 2023 Pengbo Liu, Mengke Sun, S. Kevin Zhou

Objective and Impact Statement: Accurate organ segmentation is critical for many clinical applications at different clinical sites, which may have their specific application requirements that concern different organs.

Organ Segmentation

MURPHY: Relations Matter in Surgical Workflow Analysis

no code implementations24 Dec 2022 Shang Zhao, Yanzhe Liu, Qiyuan Wang, Dai Sun, Rong Liu, S. Kevin Zhou

Autonomous robotic surgery has advanced significantly based on analysis of visual and temporal cues in surgical workflow, but relational cues from domain knowledge remain under investigation.

LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation

1 code implementation5 Dec 2022 Ziyuan Zhao, Fangcheng Zhou, Kaixin Xu, Zeng Zeng, Cuntai Guan, S. Kevin Zhou

To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images.

Image Segmentation Medical Image Segmentation +3

Information-guided pixel augmentation for pixel-wise contrastive learning

no code implementations14 Nov 2022 Quan Quan, Qingsong Yao, Jun Li, S. Kevin Zhou

To the best of our knowledge, we are the first to propose a pixel augmentation method with a pixel granularity for enhancing unsupervised pixel-wise contrastive learning.

Contrastive Learning Self-Supervised Learning

Active CT Reconstruction with a Learned Sampling Policy

no code implementations3 Nov 2022 Ce Wang, Kun Shang, Haimiao Zhang, Shang Zhao, Dong Liang, S. Kevin Zhou

Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.

Computed Tomography (CT) Decision Making

DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images

no code implementations11 Oct 2022 Cheng Peng, S. Kevin Zhou, Rama Chellappa

Medical image super-resolution (SR) is an active research area that has many potential applications, including reducing scan time, bettering visual understanding, increasing robustness in downstream tasks, etc.

Domain Adaptation Image Super-Resolution

Progress and Prospects for Fairness in Healthcare and Medical Image Analysis

no code implementations27 Sep 2022 Zikang Xu, Jun Li, Qingsong Yao, S. Kevin Zhou

Machine learning-enabled medical imaging analysis has become a vital part of the current automatic diagnosis system.

Fairness object-detection +1

Stabilize, Decompose, and Denoise: Self-Supervised Fluoroscopy Denoising

no code implementations30 Aug 2022 Ruizhou Liu, Qiang Ma, Zhiwei Cheng, Yuanyuan Lyu, Jianji Wang, S. Kevin Zhou

Fluoroscopy is an imaging technique that uses X-ray to obtain a real-time 2D video of the interior of a 3D object, helping surgeons to observe pathological structures and tissue functions especially during intervention.

Denoising Optical Flow Estimation +1

REGAS: REspiratory-GAted Synthesis of Views for Multi-Phase CBCT Reconstruction from a single 3D CBCT Acquisition

no code implementations17 Aug 2022 Cheng Peng, Haofu Liao, S. Kevin Zhou, Rama Chellappa

It is a long-standing challenge to reconstruct Cone Beam Computed Tomography (CBCT) of the lung under respiratory motion.

SATr: Slice Attention with Transformer for Universal Lesion Detection

no code implementations13 Mar 2022 Han Li, Long Chen, Hu Han, S. Kevin Zhou

Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis.

Lesion Detection

DATR: Domain-adaptive transformer for multi-domain landmark detection

no code implementations12 Mar 2022 Heqin Zhu, Qingsong Yao, S. Kevin Zhou

In this work, we propose a universal model for multi-domain landmark detection by taking advantage of transformer for modeling long dependencies and develop a domain-adaptive transformer model, named as DATR, which is trained on multiple mixed datasets from different anatomies and capable of detecting landmarks of any image from those anatomies.


DFTR: Depth-supervised Fusion Transformer for Salient Object Detection

no code implementations12 Mar 2022 Heqin Zhu, Xu sun, Yuexiang Li, Kai Ma, S. Kevin Zhou, Yefeng Zheng

This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture.

Benchmarking object-detection +2

Recovering medical images from CT film photos

no code implementations10 Mar 2022 Quan Quan, Qiyuan Wang, Yuanqi Du, Liu Li, S. Kevin Zhou

While medical images such as computed tomography (CT) are stored in DICOM format in hospital PACS, it is still quite routine in many countries to print a film as a transferable medium for the purposes of self-storage and secondary consultation.

Computed Tomography (CT)

Undersampled MRI Reconstruction with Side Information-Guided Normalisation

no code implementations7 Mar 2022 Xinwen Liu, Jing Wang, Cheng Peng, Shekhar S. Chandra, Feng Liu, S. Kevin Zhou

In this paper, we investigate the use of such side information as normalisation parameters in a convolutional neural network (CNN) to improve undersampled MRI reconstruction.

MRI Reconstruction

Rib Suppression in Digital Chest Tomosynthesis

no code implementations5 Mar 2022 Yihua Sun, Qingsong Yao, Yuanyuan Lyu, Jianji Wang, Yi Xiao, Hongen Liao, S. Kevin Zhou

Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D images of a human chest for pulmonary disease screening, with 2D X-ray projections taken within an extremely limited range of angles.

Universal Segmentation of 33 Anatomies

no code implementations4 Mar 2022 Pengbo Liu, Yang Deng, Ce Wang, Yuan Hui, Qian Li, Jun Li, Shiwei Luo, Mengke Sun, Quan Quan, Shuxin Yang, You Hao, Honghu Xiao, Chunpeng Zhao, Xinbao Wu, S. Kevin Zhou

Firstly, while it is ideal to learn such a model from a large-scale, fully-annotated dataset, it is practically hard to curate such a dataset.

Image Segmentation Medical Image Segmentation +1

MixCL: Pixel label matters to contrastive learning

no code implementations4 Mar 2022 Jun Li, Quan Quan, S. Kevin Zhou

It is essential for medical image analysis, which is often notorious for its lack of annotations.

Contrastive Learning Image Segmentation +2

Learning Incrementally to Segment Multiple Organs in a CT Image

no code implementations4 Mar 2022 Pengbo Liu, Xia Wang, Mengsi Fan, Hongli Pan, Minmin Yin, Xiaohong Zhu, Dandan Du, Xiaoying Zhao, Li Xiao, Lian Ding, Xingwang Wu, S. Kevin Zhou

In each incremental learning (IL) stage, we lose the access to previous data and annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs.

Incremental Learning Organ Segmentation

Relative distance matters for one-shot landmark detection

no code implementations3 Mar 2022 Qingsong Yao, Jianji Wang, Yihua Sun, Quan Quan, Heqin Zhu, S. Kevin Zhou

Contrastive learning based methods such as cascade comparing to detect (CC2D) have shown great potential for one-shot medical landmark detection.

Contrastive Learning

Which images to label for few-shot medical landmark detection?

no code implementations CVPR 2022 Quan Quan, Qingsong Yao, Jun Li, S. Kevin Zhou

We herein propose a novel Sample Choosing Policy (SCP) to select "the most worthy" images for annotation, in the context of few-shot medical landmark detection.

Few-Shot Learning

Medical Aegis: Robust adversarial protectors for medical images

no code implementations22 Nov 2021 Qingsong Yao, Zecheng He, S. Kevin Zhou

To the best of our knowledge, Medical Aegis is the first defense in the literature that successfully addresses the strong adaptive adversarial example attacks to medical images.

DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction

no code implementations21 Nov 2021 Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, Yuan Hui, S. Kevin Zhou

While Computed Tomography (CT) reconstruction from X-ray sinograms is necessary for clinical diagnosis, iodine radiation in the imaging process induces irreversible injury, thereby driving researchers to study sparse-view CT reconstruction, that is, recovering a high-quality CT image from a sparse set of sinogram views.

Computed Tomography (CT)

GAN-based disentanglement learning for chest X-ray rib suppression

no code implementations18 Oct 2021 Luyi Han, Yuanyuan Lyu, Cheng Peng, S. Kevin Zhou

Clinical evidence has shown that rib-suppressed chest X-rays (CXRs) can improve the reliability of pulmonary disease diagnosis.

Benchmarking Computed Tomography (CT) +2

Shallow Feature Matters for Weakly Supervised Object Localization

1 code implementation CVPR 2021 Jun Wei, Qin Wang, Zhen Li, Sheng Wang, S. Kevin Zhou, Shuguang Cui

In practice, our SPOL model first generates the CAMs through a novel element-wise multiplication of shallow and deep feature maps, which filters the background noise and generates sharper boundaries robustly.

Pseudo Label Weakly-Supervised Object Localization

Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image

no code implementations24 Jun 2021 Yuanqi Du, Quan Quan, Hu Han, S. Kevin Zhou

Pseudo-normality synthesis, which computationally generates a pseudo-normal image from an abnormal one (e. g., with lesions), is critical in many perspectives, from lesion detection, data augmentation to clinical surgery suggestion.

Data Augmentation Image Generation +2

Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement

no code implementations CVPR 2021 Hao Lu, Hu Han, S. Kevin Zhou

Remote photoplethysmography (rPPG) based physiological measurement has great application values in health monitoring, emotion analysis, etc.

Disentanglement Emotion Recognition +1

Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration

1 code implementation14 Apr 2021 Bo Zhou, Zachary Augenfeld, Julius Chapiro, S. Kevin Zhou, Chi Liu, James S. Duncan

Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations.

Anatomy Domain Adaptation +2

Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation

no code implementations9 Mar 2021 Ce Wang, Haimiao Zhang, Qian Li, Kun Shang, Yuanyuan Lyu, Bin Dong, S. Kevin Zhou

More importantly, we show that using such a sinogram extrapolation module significantly improves the generalization capability of the model on unseen datasets (e. g., COVID-19 and LIDC datasets) when compared to existing approaches.

Computed Tomography (CT)

Universal Undersampled MRI Reconstruction

no code implementations9 Mar 2021 Xinwen Liu, Jing Wang, Feng Liu, S. Kevin Zhou

Simply mixing images from multiple anatomies for training a single network does not lead to an ideal universal model due to the statistical shift among datasets of various anatomies, the need to retrain from scratch on all datasets with the addition of a new dataset, and the difficulty in dealing with imbalanced sampling when the new dataset is further of a smaller size.

Anatomy MRI Reconstruction

You Only Learn Once: Universal Anatomical Landmark Detection

2 code implementations8 Mar 2021 Heqin Zhu, Qingsong Yao, Li Xiao, S. Kevin Zhou

However, all of those methods are unary in the sense that a highly specialized network is trained for a single task say associated with a particular anatomical region.


Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets

no code implementations8 Mar 2021 Pengbo Liu, Li Xiao, S. Kevin Zhou

In each IL stage, we lose access to the previous annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs.

Incremental Learning Organ Segmentation

One-Shot Medical Landmark Detection

2 code implementations8 Mar 2021 Qingsong Yao, Quan Quan, Li Xiao, S. Kevin Zhou

The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medical images.

Self-Supervised Learning

U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction

no code implementations8 Mar 2021 Yuanyuan Lyu, Jiajun Fu, Cheng Peng, S. Kevin Zhou

Recently, both supervised and unsupervised deep learning methods have been widely applied on the CT metal artifact reduction (MAR) task.

Disentanglement Metal Artifact Reduction

Deep reinforcement learning in medical imaging: A literature review

no code implementations5 Mar 2021 S. Kevin Zhou, Hoang Ngan Le, Khoa Luu, Hien V. Nguyen, Nicholas Ayache

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequence of actions that maximizes the expected reward, with the representative power of deep neural networks.

Lesion Detection Miscellaneous +3

CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep Models

no code implementations17 Dec 2020 Quan Quan, Qiyuan Wang, Liu Li, Yuanqi Du, S. Kevin Zhou

We also record all accompanying information related to the geometric deformation (such as 3D coordinate, depth, normal, and UV maps) and illumination variation (such as albedo map).

Computed Tomography (CT)

A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks

1 code implementation17 Dec 2020 Qingsong Yao, Zecheng He, Yi Lin, Kai Ma, Yefeng Zheng, S. Kevin Zhou

Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making.

Adversarial Attack Decision Making

Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models

1 code implementation16 Dec 2020 Pengbo Liu, Hu Han, Yuanqi Du, Heqin Zhu, Yinhao Li, Feng Gu, Honghu Xiao, Jun Li, Chunpeng Zhao, Li Xiao, Xinbao Wu, S. Kevin Zhou

Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.

Aggregative Self-Supervised Feature Learning from a Limited Sample

no code implementations14 Dec 2020 Jiuwen Zhu, Yuexiang Li, S. Kevin Zhou

Then, in self-aggregative SSL, we propose to self-complement an existing proxy task with an auxiliary loss function based on a linear centered kernel alignment metric, which explicitly promotes the exploring of where are uncovered by the features learned from a proxy task at hand to further boost the modeling capability.

Image Classification Medical Image Classification +1

Label-Free Segmentation of COVID-19 Lesions in Lung CT

no code implementations8 Sep 2020 Qingsong Yao, Li Xiao, Peihang Liu, S. Kevin Zhou

Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans.

COVID-19 Diagnosis Unsupervised Anomaly Detection

Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency

no code implementations3 Sep 2020 Bo Zhou, S. Kevin Zhou, James S. Duncan, Chi Liu

To derive quality reconstruction, previous state-of-the-art methods use UNet-like neural architectures to directly predict the full view reconstruction from limited view data; but these methods leave the deep network architecture issue largely intact and cannot guarantee the consistency between the sinogram of the reconstructed image and the acquired sinogram, leading to a non-ideal reconstruction.

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.

Bounding Maps for Universal Lesion Detection

no code implementations18 Jul 2020 Han Li, Hu Han, S. Kevin Zhou

The bounding maps (BMs) are used in two-stage anchor-based ULD frameworks to reduce the FP rate.

Lesion Detection Region Proposal

Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection

1 code implementation10 Jul 2020 Qingsong Yao, Zecheng He, Hu Han, S. Kevin Zhou

A comprehensive evaluation on a public dataset for cephalometric landmark detection demonstrates that the adversarial examples generated by ATI-FGSM break the CNN-based network more effectively and efficiently, compared with the original Iterative FGSM attack.

Adversarial Attack

Marginal loss and exclusion loss for partially supervised multi-organ segmentation

no code implementations8 Jul 2020 Gonglei Shi, Li Xiao, Yang Chen, S. Kevin Zhou

Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partially labeled, that is, a dataset has a few organs labeled but not all organs.

Organ Segmentation

Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning

no code implementations10 Jun 2020 Jiuwen Zhu, Yuexiang Li, Yifan Hu, S. Kevin Zhou

To this end, self-supervised learning (SSL), as a potential solution for deficient annotated data, attracts increasing attentions from the community.

General Classification Image Classification +2

Human Recognition Using Face in Computed Tomography

no code implementations28 May 2020 Jiuwen Zhu, Hu Han, S. Kevin Zhou

With the mushrooming use of computed tomography (CT) images in clinical decision making, management of CT data becomes increasingly difficult.

Computed Tomography (CT) Decision Making +3

DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior

1 code implementation CVPR 2020 Bo Zhou, S. Kevin Zhou

In this work, we address the above two limitations by proposing a Dual Domain Recurrent Network (DuDoRNet) with deep T1 prior embedded to simultaneously recover k-space and images for accelerating the acquisition of MRI with a long imaging protocol.

MRI Reconstruction

First image then video: A two-stage network for spatiotemporal video denoising

1 code implementation2 Jan 2020 Ce Wang, S. Kevin Zhou, Zhiwei Cheng

This two-stage network, when trained in an end-to-end fashion, yields the state-of-the-art performances on the video denoising benchmark Vimeo90K dataset in terms of both denoising quality and computation.

Image Denoising Optical Flow Estimation +1

Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography

no code implementations2 Jan 2020 Yuanyuan Lyu, Wei-An Lin, Haofu Liao, Jing-Jing Lu, S. Kevin Zhou

Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain.

Computed Tomography (CT) Metal Artifact Reduction

Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition

1 code implementation16 Sep 2019 Zeju Li, Han Li, Hu Han, Gonglei Shi, Jiannan Wang, S. Kevin Zhou

We hereby propose a decomposition generative adversarial network (DecGAN) to anatomically decompose a CXR image but with unpaired data.

Anatomy Disentanglement

Towards Learning a Self-inverse Network for Bidirectional Image-to-image Translation

no code implementations9 Sep 2019 Zengming Shen, Yifan Chen, S. Kevin Zhou, Bogdan Georgescu, Xuqi Liu, Thomas S. Huang

A self-inverse network shares several distinct advantages: only one network instead of two, better generalization and more restricted parameter space.

Image-to-Image Translation Translation

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation

1 code implementation4 Sep 2019 Chao Huang, Hu Han, Qingsong Yao, Shankuan Zhu, S. Kevin Zhou

Instead of a collection of multiple models, it is highly desirable to learn a universal data representation for different tasks, ideally a single model with the addition of a minimal number of parameters steered to each task.

Image Classification Image Segmentation +3

Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement

no code implementations15 Aug 2019 Cheng Peng, Wei-An Lin, Haofu Liao, Rama Chellappa, S. Kevin Zhou

We propose a marginal super-resolution (MSR) approach based on 2D convolutional neural networks (CNNs) for interpolating an anisotropic brain magnetic resonance scan along the highly under-sampled direction, which is assumed to axial without loss of generality.

Semantic Segmentation Super-Resolution

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

2 code implementations3 Aug 2019 Haofu Liao, Wei-An Lin, S. Kevin Zhou, Jiebo Luo

Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training.

Computed Tomography (CT) Disentanglement +4

Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction

no code implementations29 Jun 2019 Haofu Liao, Wei-An Lin, Zhimin Huo, Levon Vogelsang, William J. Sehnert, S. Kevin Zhou, Jiebo Luo

A conventional approach to computed tomography (CT) or cone beam CT (CBCT) metal artifact reduction is to replace the X-ray projection data within the metal trace with synthesized data.

Computed Tomography (CT) Metal Artifact Reduction

Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

1 code implementation5 Jun 2019 Haofu Liao, Wei-An Lin, Jianbo Yuan, S. Kevin Zhou, Jiebo Luo

Extensive experiments show that our method significantly outperforms the existing unsupervised models for image-to-image translation problems, and achieves comparable performance to existing supervised models on a synthesized dataset.

Computed Tomography (CT) Disentanglement +3

Multiview 2D/3D Rigid Registration via a Point-Of-Interest Network for Tracking and Triangulation ($\text{POINT}^2$)

no code implementations10 Mar 2019 Haofu Liao, Wei-An Lin, Jiarui Zhang, Jingdan Zhang, Jiebo Luo, S. Kevin Zhou

As the POI tracker is shift-invariant, $\text{POINT}^2$ is more robust to the initial pose of the 3D pre-intervention image.

Face Completion with Semantic Knowledge and Collaborative Adversarial Learning

no code implementations8 Dec 2018 Haofu Liao, Gareth Funka-Lea, Yefeng Zheng, Jiebo Luo, S. Kevin Zhou

Unlike a conventional background inpainting approach that infers a missing area from image patches similar to the background, face completion requires semantic knowledge about the target object for realistic outputs.

Facial Inpainting Semantic Segmentation

Select, Attend, and Transfer: Light, Learnable Skip Connections

no code implementations14 Apr 2018 Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma, S. Kevin Zhou, Yefeng Zheng, Bogdan Georgescu, Puneet Sharma, Sasa Grbic, Zhoubing Xu, Dorin Comaniciu, Ghassan Hamarneh

Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients.

Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

no code implementations25 Jul 2017 Dong Yang, Daguang Xu, S. Kevin Zhou, Bogdan Georgescu, Mingqing Chen, Sasa Grbic, Dimitris Metaxas, Dorin Comaniciu

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment.

Liver Segmentation

Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images

no code implementations7 Jul 2016 Hao Chen, Yefeng Zheng, Jin-Hyeong Park, Pheng-Ann Heng, S. Kevin Zhou

Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements.

Transfer Learning

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