1.1k citations · 2k across the 46 of their papers we have counts for
26 papers · 1 filter
CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion
Fenghe Tang, Jianrui Ding, Lingtao Wang +2
The U-shaped architecture has emerged as a crucial paradigm in the design of medical image segmentation networks. However, due to the inherent local limitations of convolution, a f…
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
Qing Wu, Lixuan Chen, Ce Wang +4
Emerging neural reconstruction techniques based on tomography (e.g., NeRF, NeAT, and NeRP) have started showing unique capabilities in medical imaging. In this work, we present a n…
DiffULD: Diffusive Universal Lesion Detection
Peiang Zhao, Han Li, Ruiyang Jin +1
Universal Lesion Detection (ULD) in computed tomography (CT) plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by anchor-based detection…
Causal Image Synthesis of Brain MR in 3D
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. To this end, we present a novel method for…
Rethinking Dual-Domain Undersampled MRI reconstruction: domain-specific design from the perspective of the receptive field
Ziqi Gao, S. Kevin Zhou
Undersampled MRI reconstruction is crucial for accelerating clinical scanning. Dual-domain reconstruction network is performant among SoTA deep learning methods. In this paper, we…
O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis
Jun Li, Kexin Li, Yafeng Zhou +1
Targeted diagnosis and treatment plans for patients with coronary artery disease vary according to atherosclerotic plaque component. Coronary CT angiography (CCTA) is widely used f…
DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis
Xian Wu, Shuxin Yang, Zhaopeng Qiu +6
Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important…
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images
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 robust…
SATr: Slice Attention with Transformer for Universal Lesion Detection
Han Li, Long Chen, Hu Han +1
Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by multi-slice-input detection…
Towards performant and reliable undersampled MR reconstruction via diffusion model sampling
Cheng Peng, Pengfei Guo, S. Kevin Zhou +2
Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time. To this end, current State-of-The-Art (SoTA) approaches leverage deep neu…
Recovering medical images from CT film photos
Quan Quan, Qiyuan Wang, Yuanqi Du +2
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 med…
Undersampled MRI Reconstruction with Side Information-Guided Normalisation
Xinwen Liu, Jing Wang, Cheng Peng +3
Magnetic resonance (MR) images exhibit various contrasts and appearances based on factors such as different acquisition protocols, views, manufacturers, scanning parameters, etc. T…
Rib Suppression in Digital Chest Tomosynthesis
Yihua Sun, Qingsong Yao, Yuanyuan Lyu +4
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 extremel…
3D endoscopic depth estimation using 3D surface-aware constraints
Shang Zhao, Ce Wang, Qiyuan Wang +2
Robotic-assisted surgery allows surgeons to conduct precise surgical operations with stereo vision and flexible motor control. However, the lack of 3D spatial perception limits sit…
MixCL: Pixel label matters to contrastive learning
Jun Li, Quan Quan, S. Kevin Zhou
Contrastive learning and self-supervised techniques have gained prevalence in computer vision for the past few years. It is essential for medical image analysis, which is often not…
Learning Incrementally to Segment Multiple Organs in a CT Image
Pengbo Liu, Xia Wang, Mengsi Fan +9
There exists a large number of datasets for organ segmentation, which are partially annotated and sequentially constructed. A typical dataset is constructed at a certain time by cu…
Universal Segmentation of 33 Anatomies
Pengbo Liu, Yang Deng, Ce Wang +12
In the paper, we present an approach for learning a single model that universally segments 33 anatomical structures, including vertebrae, pelvic bones, and abdominal organs. Our mo…
U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction
Yuanyuan Lyu, Jiajun Fu, Cheng Peng +1
Recently, both supervised and unsupervised deep learning methods have been widely applied on the CT metal artifact reduction (MAR) task. Supervised methods such as Dual Domain Netw…
Deep reinforcement learning in medical imaging: A literature review
S. Kevin Zhou, Hoang Ngan Le, Khoa Luu +2
Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequence of actions that maximizes the expected reward, with the representative powe…
Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency
Bo Zhou, S. Kevin Zhou, James S. Duncan +1
Limited view tomographic reconstruction aims to reconstruct a tomographic image from a limited number of sinogram or projection views arising from sparse view or limited angle acqu…
DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior
Bo Zhou, S. Kevin Zhou
MRI with multiple protocols is commonly used for diagnosis, but it suffers from a long acquisition time, which yields the image quality vulnerable to say motion artifacts. To accel…
ADSegNet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation
Yuanyuan Lyu, Haofu Liao, Heqin Zhu +1
Spinal surgery planning necessitates automatic segmentation of vertebrae in cone-beam computed tomography (CBCT), an intraoperative imaging modality that is widely used in interven…
Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition
Zeju Li, Han Li, Hu Han +3
Although chest X-ray (CXR) offers a 2D projection with overlapped anatomies, it is widely used for clinical diagnosis. There is clinical evidence supporting that decomposing an X-r…
Towards multi-sequence MR image recovery from undersampled k-space data
Cheng Peng, Wei-An Lin, Rama Chellappa +1
Undersampled MR image recovery has been widely studied for accelerated MR acquisition. However, it has been mostly studied under a single sequence scenario, despite the fact that m…