Search Results for author:
Found 19 papers, 5 papers with code
XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention
An effective backbone network is important to deep learning-based Deformable Medical Image Registration (DMIR), because it extracts and matches the features between two images to discover the mutual correspondence for fine registration.
Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration
We present a novel deep learning-based framework: Embedded Feature Similarity Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical image registration which is a most challenging problem due to the difficulty such as dimensional mismatch, heavy computation load and lack of golden evaluation standard.
Multiscale Low-Frequency Memory Network for Improved Feature Extraction in Convolutional Neural Networks
Responding to these complexities, we introduce a novel framework, the Multiscale Low-Frequency Memory (MLFM) Network, with the goal to harness the full potential of CNNs while keeping their complexity unchanged.
Fully Differentiable Correlation-driven 2D/3D Registration for X-ray to CT Image Fusion
Image-based rigid 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions.
MomentsNet: a simple learning-free method for binary image recognition
In this paper, we propose a new simple and learning-free deep learning network named MomentsNet, whose convolution layer, nonlinear processing layer and pooling layer are constructed by Moments kernels, binary hashing and block-wise histogram, respectively.
PCANet: An energy perspective
The principal component analysis network (PCANet), which is one of the recently proposed deep learning architectures, achieves the state-of-the-art classification accuracy in various databases.
Fractional spectral graph wavelets and their applications
One of the key challenges in the area of signal processing on graphs is to design transforms and dictionaries methods to identify and exploit structure in signals on weighted graphs.
Modulated binary cliquenet
Although Convolutional Neural Networks (CNNs) achieve effectiveness in various computer vision tasks, the significant requirement of storage of such networks hinders the deployment on computationally limited devices.
Compressing complex convolutional neural network based on an improved deep compression algorithm
Although convolutional neural network (CNN) has made great progress, large redundant parameters restrict its deployment on embedded devices, especially mobile devices.
Deep Octonion Networks
In recent years, the deep complex networks (DCNs) and the deep quaternion networks (DQNs) have attracted more and more attentions.
CSLNSpeech: solving extended speech separation problem with the help of Chinese sign language
We design a general deep learning network for learning the combination of three modalities, audio, face, and sign language information, for better solving the speech separation problem.
Generative networks as inverse problems with fractional wavelet scattering networks
In order to solve or alleviate the synchronous training difficult problems of GANs and VAEs, recently, researchers propose Generative Scattering Networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain the features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate the image.
Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images
Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation.
EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation
3)We propose the adversarial weighted ensemble module which uses the trained discriminators to evaluate the quality of segmented structures, and normalizes these evaluation scores for the ensemble weights directed at the input image, thus enhancing the ensemble results.
CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Compartments Segmentation on CT Images
We propose a novel weakly supervised learning framework, Cycle Prototype Network, for 3D renal compartment segmentation.
SpineCLUE: Automatic Vertebrae Identification Using Contrastive Learning and Uncertainty Estimation
In this paper, we propose a three-stage method to address the challenges in 3D CT vertebrae identification at vertebrae-level.
An Optimization-based Baseline for Rigid 2D/3D Registration Applied to Spine Surgical Navigation Using CMA-ES
While artificial intelligence technology has advanced rapidly in recent years, traditional optimization-based registration methods remain indispensable in the field of 2D/3D registration. he exceptional precision of this method enables it to be considered as a post-processing step of the learning-based methods, thereby offering a reliable assurance for registration.
Rethinking Referring Object Removal
Referring object removal refers to removing the specific object in an image referred by natural language expressions and filling the missing region with reasonable semantics.
ST-LDM: A Universal Framework for Text-Grounded Object Generation in Real Images
We transcend the limitation of traditional attention mechanisms that only focus on existing visual features by introducing deformable feature alignment to hierarchically refine spatial positioning fused with multi-scale visual and linguistic information.
