b'S. Kevin Zhou'

research

∙ 06/02/2022

Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives

Transformer, the latest technological advance of deep learning, has gain...

Jun Li, et al. ∙

research

∙ 03/13/2022

SATr: Slice Attention with Transformer for Universal Lesion Detection

Universal Lesion Detection (ULD) in computed tomography plays an essenti...

Han Li, et al. ∙

research

∙ 03/12/2022

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

Accurate anatomical landmark detection plays an increasingly vital role ...

Heqin Zhu, et al. ∙

research

∙ 03/12/2022

DFTR: Depth-supervised Hierarchical Feature Fusion Transformer for Salient Object Detection

Automated salient object detection (SOD) plays an increasingly crucial r...

Heqin Zhu, et al. ∙

research

∙ 03/10/2022

Recovering medical images from CT film photos

While medical images such as computed tomography (CT) are stored in DICO...

Quan Quan, et al. ∙

research

∙ 03/08/2022

Towards performant and reliable undersampled MR reconstruction via diffusion model sampling

Magnetic Resonance (MR) image reconstruction from under-sampled acquisit...

Cheng Peng, et al. ∙

research

∙ 03/07/2022

Undersampled MRI Reconstruction with Side Information-Guided Normalisation

Magnetic resonance (MR) images exhibit various contrasts and appearances...

Xinwen Liu, et al. ∙

research

∙ 03/05/2022

Rib Suppression in Digital Chest Tomosynthesis

Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D...

Yihua Sun, et al. ∙

research

∙ 03/04/2022

3D endoscopic depth estimation using 3D surface-aware constraints

Robotic-assisted surgery allows surgeons to conduct precise surgical ope...

Shang Zhao, et al. ∙

research

∙ 03/04/2022

MixCL: Pixel label matters to contrastive learning

Contrastive learning and self-supervised techniques have gained prevalen...

Jun Li, et al. ∙

research

∙ 03/04/2022

Learning Incrementally to Segment Multiple Organs in a CT Image

There exists a large number of datasets for organ segmentation, which ar...

Pengbo Liu, et al. ∙

research

∙ 03/04/2022

Universal Segmentation of 33 Anatomies

In the paper, we present an approach for learning a single model that un...

Pengbo Liu, et al. ∙

research

∙ 03/03/2022

Relative distance matters for one-shot landmark detection

Contrastive learning based methods such as cascade comparing to detect (...

Qingsong Yao, et al. ∙

research

∙ 12/30/2021

Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment

In clinics, a radiology report is crucial for guiding a patient's treatm...

Shuxin Yang, et al. ∙

research

∙ 12/07/2021

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

The success of deep learning methods relies on the availability of well-...

Quan Quan, et al. ∙

research

∙ 11/22/2021

Medical Aegis: Robust adversarial protectors for medical images

Deep neural network based medical image systems are vulnerable to advers...

Qingsong Yao, et al. ∙

research

∙ 11/21/2021

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

While Computed Tomography (CT) reconstruction from X-ray sinograms is ne...

Ce Wang, et al. ∙

research

∙ 10/18/2021

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

Clinical evidence has shown that rib-suppressed chest X-rays (CXRs) can ...

Luyi Han, et al. ∙

research

∙ 08/02/2021

Shallow Attention Network for Polyp Segmentation

Accurate polyp segmentation is of great importance for colorectal cancer...

Jun Wei, et al. ∙

research

∙ 06/24/2021

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

Pseudo-normality synthesis, which computationally generates a pseudo-nor...

Yuanqi Du, et al. ∙

research

∙ 05/31/2021

CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

Spine-related diseases have high morbidity and cause a huge burden of so...

Yang Deng, et al. ∙

research

∙ 04/14/2021

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

Multimodal image registration has many applications in diagnostic medica...

Bo Zhou, et al. ∙

research

∙ 03/23/2021

Conditional Training with Bounding Map for Universal Lesion Detection

Universal Lesion Detection (ULD) in computed tomography plays an essenti...

Han Li, et al. ∙

research

∙ 03/09/2021

Generalizable Limited-Angle CT Reconstruction via Sinogram Extrapolation

Computed tomography (CT) reconstruction from X-ray projections acquired ...

Ce Wang, et al. ∙

research

∙ 03/09/2021

Universal Undersampled MRI Reconstruction

Deep neural networks have been extensively studied for undersampled MRI ...

Xinwen Liu, et al. ∙

research

∙ 03/08/2021

You Only Learn Once: Universal Anatomical Landmark Detection

Detecting anatomical landmarks in medical images plays an essential role...

Heqin Zhu, et al. ∙

research

∙ 03/08/2021

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

Recently, both supervised and unsupervised deep learning methods have be...

Yuanyuan Lyu, et al. ∙

research

∙ 03/08/2021

One-Shot Medical Landmark Detection

The success of deep learning methods relies on the availability of a lar...

Qingsong Yao, et al. ∙

research

∙ 03/08/2021

Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets

There exists a large number of datasets for organ segmentation, which ar...

Pengbo Liu, et al. ∙

research

∙ 03/05/2021

Deep reinforcement learning in medical imaging: A literature review

Deep reinforcement learning (DRL) augments the reinforcement learning fr...

S. Kevin Zhou, et al. ∙

research

∙ 12/17/2020

A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks

Deep neural networks (DNNs) for medical images are extremely vulnerable ...

Qingsong Yao, et al. ∙

research

∙ 12/17/2020

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

While medical images such as computed tomography (CT) are stored in DICO...

Quan Quan, et al. ∙

research

∙ 12/16/2020

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

Purpose: Pelvic bone segmentation in CT has always been an essential ste...

Pengbo Liu, et al. ∙

research

∙ 12/14/2020

Aggregative Self-Supervised Feature Learning

Self-supervised learning (SSL) is an efficient approach that addresses t...

Jiuwen Zhu, et al. ∙

research

∙ 09/08/2020

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

Scarcity of annotated images hampers the building of automated solution ...

Qingsong Yao, et al. ∙

research

∙ 09/03/2020

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

Limited view tomographic reconstruction aims to reconstruct a tomographi...

Bo Zhou, et al. ∙

research

∙ 07/18/2020

Bounding Maps for Universal Lesion Detection

Universal Lesion Detection (ULD) in computed tomography plays an essenti...

Han Li, et al. ∙

research

∙ 07/10/2020

Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection

Recent methods in multiple landmark detection based on deep convolutiona...

Qingsong Yao, et al. ∙

research

∙ 07/08/2020

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

Annotating multiple organs in medical images is both costly and time-con...

Gonglei Shi, et al. ∙

research

∙ 06/10/2020

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

Deep learning highly relies on the amount of annotated data. However, an...

Jiuwen Zhu, et al. ∙

research

∙ 05/28/2020

Human Recognition Using Face in Computed Tomography

With the mushrooming use of computed tomography (CT) images in clinical ...

Jiuwen Zhu, et al. ∙

research

∙ 01/11/2020

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

MRI with multiple protocols is commonly used for diagnosis, but it suffe...

Bo Zhou, et al. ∙

research

∙ 01/02/2020

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

Video denoising is to remove noise from noise-corrupted data, thus recov...

Ce Wang, et al. ∙

research

∙ 01/02/2020

DuDoNet++: Encoding mask projection to reduce CT metal artifacts

CT metal artifact reduction (MAR) is a notoriously challenging task beca...

Yuanyuan Lyu, et al. ∙

research

∙ 01/02/2020

Joint Unsupervised Learning for the Vertebra Segmentation, Artifact Reduction and Modality Translation of CBCT Images

We investigate the unsupervised learning of the vertebra segmentation, a...

Yuanyuan Lyu, et al. ∙

research

∙ 09/09/2019

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

Recently image-to-image translation has attracted significant interests ...

Zengming Shen, et al. ∙

research

∙ 09/09/2019

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

The one-to-one mapping is necessary for many bidirectional image-to-imag...

Zengming Shen, et al. ∙

research

∙ 08/15/2019

Towards multi-sequence MR image recovery from undersampled k-space data

Undersampled MR image recovery has been widely studied for accelerated M...

Cheng Peng, et al. ∙

research

∙ 08/15/2019

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

We propose a marginal super-resolution (MSR) approach based on 2D convol...

Cheng Peng, et al. ∙

research

∙ 08/03/2019

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

Current deep neural network based approaches to computed tomography (CT)...

Haofu Liao, et al. ∙

S. Kevin Zhou

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