no code implementations • 15 Mar 2024 • Xiangtian Xue, Jiasong Wu, Youyong Kong, Lotfi Senhadji, Huazhong Shu
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.
no code implementations • 14 Mar 2024 • Xiangtian Xue, Jiasong Wu, Youyong Kong, Lotfi Senhadji, Huazhong Shu
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.
1 code implementation • 13 Mar 2024 • Fuzhi Wu, Jiasong Wu, Youyong Kong, Chunfeng Yang, Guanyu Yang, Huazhong Shu, Guy Carrault, Lotfi Senhadji
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.
no code implementations • 8 Feb 2024 • Minheng Chen, Tonglong Li, Zhirun Zhang, Youyong Kong
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.
1 code implementation • 4 Feb 2024 • Minheng Chen, Zhirun Zhang, Shuheng Gu, Zhangyang Ge, Youyong Kong
Image-based rigid 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions.
no code implementations • 14 Jan 2024 • Sheng Zhang, Minheng Chen, Junxian Wu, Ziyue Zhang, Tonglong Li, Cheng Xue, Youyong Kong
In this paper, we propose a three-stage method to address the challenges in 3D CT vertebrae identification at vertebrae-level.
1 code implementation • 10 May 2023 • Minheng Chen, Zhirun Zhang, Shuheng Gu, Youyong Kong
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.
1 code implementation • 15 Jun 2022 • Jiacheng Shi, Yuting He, Youyong Kong, Jean-Louis Coatrieux, Huazhong Shu, Guanyu Yang, Shuo Li
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.
no code implementations • 15 Aug 2021 • Song Wang, Yuting He, Youyong Kong, Xiaomei Zhu, Shaobo Zhang, Pengfei Shao, Jean-Louis Dillenseger, Jean-Louis Coatrieux, Shuo Li, Guanyu Yang
We propose a novel weakly supervised learning framework, Cycle Prototype Network, for 3D renal compartment segmentation.
no code implementations • 8 Jun 2021 • Yuting He, Rongjun Ge, Xiaoming Qi, Guanyu Yang, Yang Chen, Youyong Kong, Huazhong Shu, Jean-Louis Coatrieux, Shuo Li
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.
no code implementations • ECCV 2020 • Yuting He, Tiantian Li, Guanyu Yang, Youyong Kong, Yang Chen, Huazhong Shu, Jean-Louis Coatrieux, Jean-Louis Dillenseger, Shuo Li
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.
no code implementations • 28 Jul 2020 • Jiasong Wu, Jing Zhang, Fuzhi Wu, Youyong Kong, Guanyu Yang, Lotfi Senhadji, Huazhong Shu
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.
1 code implementation • 21 Jul 2020 • Jiasong Wu, Xuan Li, Taotao Li, Fanman Meng, Youyong Kong, Guanyu Yang, Lotfi Senhadji, Huazhong Shu
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.
no code implementations • 20 Mar 2019 • Jiasong Wu, Ling Xu, Youyong Kong, Lotfi Senhadji, Huazhong Shu
In recent years, the deep complex networks (DCNs) and the deep quaternion networks (DQNs) have attracted more and more attentions.
no code implementations • 6 Mar 2019 • Jiasong Wu, Hongshan Ren, Youyong Kong, Chunfeng Yang, Lotfi Senhadji, Huazhong Shu
Although convolutional neural network (CNN) has made great progress, large redundant parameters restrict its deployment on embedded devices, especially mobile devices.
no code implementations • 27 Feb 2019 • Jinpeng Xia, Jiasong Wu, Youyong Kong, Pinzheng Zhang, Lotfi Senhadji, Huazhong Shu
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.
no code implementations • 27 Feb 2019 • Jiasong Wu, Fuzhi Wu, Qihan Yang, Youyong Kong, Xilin Liu, Yan Zhang, Lotfi Senhadji, Huazhong Shu
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.
no code implementations • 22 Feb 2017 • Jiasong Wu, Shijie Qiu, Youyong Kong, Yang Chen, Lotfi Senhadji, Huazhong Shu
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.
no code implementations • 3 Mar 2016 • Jiasong Wu, Shijie Qiu, Youyong Kong, Longyu Jiang, Lotfi Senhadji, Huazhong Shu
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.