260 citations · 1.5k across the 65 of their papers we have counts for
16 papers · 1 filter
SAMAug: Point Prompt Augmentation for Segment Anything Model
Haixing Dai, Chong Ma, Zhiling Yan +14
This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generat…
Unsupervised Image Denoising with Score Function
Yutong Xie, Mingze Yuan, Bin Dong +1
Though achieving excellent performance in some cases, current unsupervised learning methods for single image denoising usually have constraints in applications. In this paper, we p…
Diffusion Model for Generative Image Denoising
Yutong Xie, Minne Yuan, Bin Dong +1
In supervised learning for image denoising, usually the paired clean images and noisy images are collected or synthesised to train a denoising model. L2 norm loss or other distance…
MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis
Mo Zhang, Quanzheng Li
Breast cancer is one of the most common cancers in women worldwide, and early detection can significantly reduce the mortality rate of breast cancer. It is crucial to take multi-sc…
ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning
Mo Zhang, Jie Zhao, Xiang Li +2
Extracting multi-scale information is key to semantic segmentation. However, the classic convolutional neural networks (CNNs) encounter difficulties in achieving multi-scale inform…
Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model
Jie Zhao, Quanzheng Li, Xiang Li +2
Pap smear testing has been widely used for detecting cervical cancers based on the morphology properties of cell nuclei in microscopic image. An accurate nuclei segmentation could…
Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction
Dufan Wu, Kyungsang Kim, Quanzheng Li
Deep-neural-network-based image reconstruction has demonstrated promising performance in medical imaging for under-sampled and low-dose scenarios. However, it requires large amount…
Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning
Jiasha Liu, Xiang Li, Hui Ren +1
Cardiovascular disease accounts for 1 in every 4 deaths in United States. Accurate estimation of structural and functional cardiac parameters is crucial for both diagnosis and dise…
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network
Kuang Gong, Kyungsang Kim, Jianan Cui +4
Recently deep neural networks have been widely and successfully applied in computer vision tasks and attracted growing interests in medical imaging. One barrier for the application…
Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
Yu Zhao, Xiang Li, Wei Zhang +5
Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cogniti…
Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes
Zhe Guo, Xiang Li, Heng Huang +2
Image analysis using more than one modality (i.e. multi-modal) has been increasingly applied in the field of biomedical imaging. One of the challenges in performing the multimodal…
End-to-end Lung Nodule Detection in Computed Tomography
Dufan Wu, Kyungsang Kim, Bin Dong +2
Computer aided diagnostic (CAD) system is crucial for modern med-ical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Co…
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
Kuang Gong, Jiahui Guan, Kyungsang Kim +4
PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and su…
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Yiping Lu, Aoxiao Zhong, Quanzheng Li +1
In our work, we bridge deep neural network design with numerical differential equations. We show that many effective networks, such as ResNet, PolyNet, FractalNet and RevNet, can b…
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
Xiang Li, Aoxiao Zhong, Ming Lin +6
Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by th…
A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising
Dufan Wu, Kyungsang Kim, Georges El Fakhri +1
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods…