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arXiv 2025年

作者： Shao, Liangjing Chen, Benshuang Zhao, Shuting Chen, Xinrong Academy for Engineering & Technology Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer-Assisted Intervention Fudan University China

Real-time ego-motion tracking for endoscope is a significant task for efficient navigation and robotic automation of endoscopy. In this paper, a novel framework is proposed to perform real-time ego-motion tracking for endoscope. Firstly, a multi-modal visual feature learning network is proposed to perform relative pose prediction, in which the motion feature from the optical flow, the scene features and the joint feature from two adjacent observations are all extracted for prediction. Due to more correlation information in the channel dimension of the concatenated image, a novel feature extractor is designed based on an attention mechanism to integrate multi-dimensional information from the concatenation of two continuous frames. To extract more complete feature representation from the fused features, a novel pose decoder is proposed to predict the pose transformation from the concatenated feature map at the end of the framework. At last, the absolute pose of endoscope is calculated based on relative poses. The experiment is conducted on three datasets of various endoscopic scenes and the results demonstrate that the proposed method outperforms state-of-the-art methods. Besides, the inference speed of the proposed method is over 30 frames per second, which meets the real-time requirement. The project page is here: *** © 2025, CC BY.

关键词： Endoscopy

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NaMA-Mamba: Foundation model for generalizable nasal disease detection using masked autoencoder with Mamba on endoscopic images

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computerized medical Imaging and Graphics 2025年 122卷 102524-102524页

作者： Wang, Wensheng Jin, Zewen Liu, Xueli Chen, Xinrong Academy for Engineering and Technology Fudan University Shanghai200433 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Eye & ENT Hospital of Fudan University Shanghai200031 China

Artificial intelligence (AI) has shown great promise in analyzing nasal endoscopic images for disease detection. However, current AI systems require extensive expert-labeled data for each specific medical condition, limiting their applications. In this work, the challenge is addressed through two key innovations, the creation of the first large-scale pre-training dataset of nasal endoscopic images, and the development of a novel self-learning AI system specifically designed for nasal endoscopy, named NaMA-Mamba. In the proposed NaMA-Mamba model, two key technologies are utilized, which are the nasal endoscopic state space model (NE-SSM) for analyzing sequences of images and an enhanced learning mechanism (CoMAE) for capturing fine details in nasal tissues. These innovations enable the system to learn effectively from unlabeled images while maintaining high accuracy across different diagnostic tasks. In extensive testing, NaMA-Mamba achieved remarkable results using minimal labeled data, matching the performance of traditional systems that require full expert labeling while needing only 1% of the labeled data for tasks such as detecting nasal polyps and identifying nasopharyngeal cancer. These results demonstrate the potential of NaMA-Mamba to significantly improve the efficiency and accessibility of AI-assisted nasal disease diagnosis in clinical practice. © 2025 Elsevier Ltd

关键词： Self-supervised learning

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Exploring CLIP’s Dense Knowledge for Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2025年

作者： Yang, Zhiwei Meng, Yucong Fu, Kexue Tang, Feilong Wang, Shuo Song, Zhijian Academy for Engineering and Technology Fudan University Shanghai200433 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shandong Computer Science Center National Supercomputer Center in Jinan China

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Activation Maps (CAMs). Recently, Contrastive Language-image Pre-training (CLIP) has been introduced in WSSS. However, recent methods primarily focus on image-text alignment for CAM generation, while CLIP’s potential in patch-text alignment remains unexplored. In this work, we propose ExCEL to explore CLIP’s dense knowledge via a novel patch-text alignment paradigm for WSSS. Specifically, we propose Text Semantic Enrichment (TSE) and Visual Calibration (VC) modules to improve the dense alignment across both text and vision modalities. To make text embeddings semantically informative, our TSE module applies Large Language Models (LLMs) to build a dataset-wide knowledge base and enriches the text representations with an implicit attribute-hunting process. To mine fine-grained knowledge from visual features, our VC module first proposes Static Visual Calibration (SVC) to propagate fine-grained knowledge in a non-parametric manner. Then Learnable Visual Calibration (LVC) is further proposed to dynamically shift the frozen features towards distributions with diverse semantics. With these enhancements, ExCEL not only retains CLIP’s training-free advantages but also significantly outperforms other state-of-the-art methods with much less training cost on PASCAL VOC and MS COCO. Code is available at https://***/zwyang6/ExCEL. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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DM-Mamba: Dual-domain Multi-scale Mamba for MRI Reconstruction

arXiv

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arXiv 2025年

作者： Meng, Yucong Yang, Zhiwei Song, Zhijian Shi, Yonghong Fu, Kexue Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Academy of Engineering and Technology Fudan University Shanghai200433 China Jinan China

The accelerated MRI reconstruction poses a challenging ill-posed inverse problem due to the significant undersampling in k-space. Deep neural networks, such as CNNs and ViT, have shown substantial performance improvements for this task while encountering the dilemma between global receptive fields and efficient computation. To this end, this paper pioneers exploring Mamba, a new paradigm for long-range dependency modeling with linear complexity, for efficient and effective MRI reconstruction. However, directly applying Mamba to MRI reconstruction faces three significant issues: (1) Mamba’s row-wise and column-wise scanning disrupts k-space’s unique spectrum, leaving its potential in k-space learning unexplored. (2) Existing Mamba methods unfold feature maps with multiple lengthy scanning paths, leading to long-range forgetting and high computational burden. (3) Mamba struggles with spatially-varying contents, resulting in limited diversity of local representations. To address these, we propose a dual-domain multi-scale Mamba for MRI reconstruction from the following perspectives: (1) We pioneer vision Mamba in k-space learning. A circular scanning is customized for spectrum unfolding, benefiting the global modeling of k-space. (2) We propose a multi-scale Mamba with an efficient scanning strategy in both image and k-space domains. It mitigates long-range forgetting and achieves a better trade-off between efficiency and performance. (3) We develop a local diversity enhancement module to improve the spatially-varying representation of Mamba. Extensive experiments are conducted on three public datasets for MRI reconstruction under various undersampling patterns. Comprehensive results demonstrate that our method significantly outperforms state-of-the-art methods with lower computational cost. Implementation code will be available in https://***/XiaoMengLiLiLi/DM-Mamba. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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Decoupled deep hough voting for point cloud registration

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Frontiers of computer Science 2024年第2期18卷 147-155页

作者： Mingzhi YUAN Kexue FU Zhihao LI Manning WANG Digital Medical Research Center School of Basic Medical SciencesFudan UniversityShanghai 200032China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai 200032China

Estimating rigid transformation using noisy correspondences is critical to feature-based point cloud ***,a series of studies have attempted to combine traditional robust model fitting with deep *** them,DHVR proposed a hough voting-based method,achieving new state-of-the-art ***,we find voting on rotation and translation simultaneously hinders achieving better ***,we proposed a new hough voting-based method,which decouples rotation and translation ***,we first utilize hough voting and a neural network to estimate *** based on good initialization on rotation,we can easily obtain accurate rigid *** experiments on 3DMatch and 3DLoMatch datasets show that our method achieves comparable performances over the state-of-the-art *** further demonstrate the generalization of our method by experimenting on KITTI dataset.

关键词： point cloud registration robust model fitting deep learning hough voting

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A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation

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Frontiers of computer Science 2023年第6期17卷 147-149页

作者： Shaolei LIU Xiaoyuan LUO Kexue FU Manning WANG Zhijian SONG Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai 200032China Digital Medical Research Center School of Basic Medical ScienceFudan UniversityShanghai 200032China

1 Introduction Deep neural networks have exhibited excellent performance in supervised tasks on point clouds,such as classification,segmentation[1]and registration[2].In supervised learning schemes,manual labeling of massive point clouds is needed for model ***,point clouds captured from different scenarios exist inevitable distribution discrepancy,and model trained from one domain always generalize badly in another *** reduce the doamin distribution discrepancy,many studies[3–6]have emerged for point cloud unsupervised domain adaptation(UDA)by learning domain-invariant features,where[5]proposed using adaptive nodes to align the local features between the source and the target domains[3,4],and[6]proposed utilizing self-supervised tasks to help capture highly transferable feature representations.

关键词： point cloud utilizing

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SS-Pro:a simplified siamese contrastive learning approach for protein surface representation

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Frontiers of computer Science 2024年第5期18卷 243-245页

作者： Ao SHEN Mingzhi YUAN Yingfan MA Manning WANG Digital Medical Research Center School of Basic Medical ScienceFudan UniversityShanghai 200032China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai 200032China

Protein surface serves as an important representation of protein structure,revealing how protein interacts with other biomolecules to perform its *** forms the basis for pharmaceutical and fundamental biological research[1].Datadriven deep learning methods in protein surface representation face challenges of label scarcity,since labeled data are typically obtained through wet lab experiments.

关键词： revealing representation simplified

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Research of an electromagnetic tracking method based on two electrically-controlled magnetic sources

Research of an electromagnetic tracking method based on two ...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015

作者： Sha, Min Wang, Yifeng Ding, Ning Fang, Zuxiang Wu, Xiaomei Department of Electronic Engineering Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai China

ISBN: (纸本)9781479919796

This paper presents an electromagnetic tracking method based on two electrically-controlled magnetic sources. Each source consists of three mutual orthogonal coils. The method can skillfully determine the rotation angles of the maximum magnetic flux density vector (MMFD V) generated by a magnetic source according to the Biot-Savart Law. Then the position of the sensor can be calculated with a noniterative geometrical algorithm in real time. Simulation results demonstrate a good performance of this method. © 2015 IEEE.

关键词： Finite element method

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Interoperative fundus image and report sharing in compliance with integrating the healthcare enterprise conformance and web access to digital imaging and communication in medicine persistent object protocol

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International Journal of Ophthalmology(English edition) 2013年第6期6卷 879-883页

作者： Hui-Qun Wu Zheng-Min Lv Xing-Yun Geng Kui Jiang Le-Min Tang Guo-Min Zhou Jian-Cheng Dong Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention(MICCAI) of Shanghai Department of Medical Informatics Medical School of Nantong University Department of Medical Image Engineering Medical School of Nantong University Department of Anatomy Histology and EmbryologySchool of Basic Medical Sciences

AIM:To address issues in interoperability between different fundus image systems,we proposed a web eyepicture archiving and communication system(PACS)framework in conformance with digital imaging and communication in medicine(DICOM)and health level 7(HL7)protocol to realize fundus images and reports sharing and communication through ***:Firstly,a telemedicine-based eye care work flow was established based on integrating the healthcare enterprise(IHE)Eye Care technical ***,a browser/server architecture eye-PACS system was established in conformance with the web access to DICOM persistent object(WADO)protocol,which contains three ***:In any client system installed with web browser,clinicians could log in the eye-PACS to observe fundus images and *** internet mail extensions(MIME)type of a structured report is saved as pdf/html with reference link to relevant fundus image using the WADO syntax could provide enough information for *** functions provided by open-source Oviyam could be used to query,zoom,move,measure,view OICOM fundus ***:Such web eye-PACS in compliance to WADO protocol could be used to store and communicate fundus images and reports,therefore is of great significance for teleophthalmology.

关键词： picture archiving and communication system teleophthalmology integrating the healthcare enterprise web access to DICOM persistent object

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Dual Attention Poser: Dual Path Body Tracking Based on Attention

Dual Attention Poser: Dual Path Body Tracking Based on Atten...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Di, Xinhan Dai, Xiaokun Zhang, Xinkang Chen, Xinrong Fudan Universiry Academy for Engineering&Technology China Deepearthgo Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China

ISBN: (纸本)9798350302493

Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users' virtual representation and experience is limited due to high reconstruction error when simple transformer network architecture is applied. In this paper, we present a novel model, named Dual Attention Poser, which can learn the whole body reconstruction at a high accuracy. The proposed model consists of three key modules. Among them, dual-path attention encoder is designed to extract feature of the sparse signals. Cross attention mixer module enable the fusion of representation in the double path. Attention-gated-mlp decoder is applied to decode the hidden feature from the sparse input through attention gate. Test results on the AMASS dataset show that Dual Attention Poser can reduce the error by up to 18.2% in comparison with the state-of-the-art results. © 2023 IEEE.

关键词： Network architecture

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