1.1k citations · 2k across the 46 of their papers we have counts for
15 papers · 1 filter
BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations
Jun Wei, Yiwen Hu, Guanbin Li +3
Accurate polyp segmentation is of great importance for colorectal cancer diagnosis and treatment. However, due to the high cost of producing accurate mask annotations, existing pol…
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…
MONAI: An open-source framework for deep learning in healthcare
M. Jorge Cardoso, Wenqi Li, Richard Brown +53
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagn…
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…
DFTR: Depth-supervised Fusion Transformer for Salient Object Detection
Heqin Zhu, Xu Sun, Yuexiang Li +3
Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than…
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…
DATR: Domain-adaptive transformer for multi-domain landmark detection
Heqin Zhu, Qingsong Yao, S. Kevin Zhou
Accurate anatomical landmark detection plays an increasingly vital role in medical image analysis. Although existing methods achieve satisfying performance, they are mostly based o…
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…