检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Qu, Linhao Liu, Shaolei Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China

In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images. We design three self-supervised reconstruction tasks according to the characteristics of multi-exposure images and conduct these tasks simultaneously using multi-task learning;through this process, the network can learn the characteristics of multi-exposure images and extract more generalized features. In addition, to compensate for the defect in establishing long-range dependencies in CNN-based architectures, we design an encoder that combines a CNN module with a transformer module. This combination enables the network to focus on both local and global information. We evaluated our method and compared it to 11 competitive traditional and deep learning-based methods on the latest released multi-exposure image fusion benchmark dataset, and our method achieved the best performance in both subjective and objective evaluations. Copyright © 2021, The Authors. All rights reserved.

关键词： image fusion

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key-Point Based Automated Diagnosis for Alveolar Dehiscence in Mandibular Incisors Using Convolutional Neural Network

SSRN

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SSRN 2022年

作者： Liu, Tianyu Ye, Yingzhi Liu, Chengcheng Chen, Jing Liu, Yuehua Xing, Wenyu Ta, Dean Academy for Engineering and Technology Fudan University Shanghai200433 China Department of Orthodontics Shanghai Stomatological Hospital School of Stomatology Fudan University Shanghai200001 China Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases Fudan University Shanghai200001 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Center for Medical Engineering School of Information Science and Technology Fudan University Shanghai200438 China

The aim of this study was to propose an automated diagnosis method for alveolar dehiscence in the anterior teeth using convolutional neural network (CNN).The Cone-beam computed tomography (CBCT) scanning was performed on 387 orthodontic patients at shanghai Stomatological Hospital. A total of 1017 mandibular incisors with the largest labiolingual sectional images were obtained from the CBCT data. We proposed two strategies of automated diagnosis methods for alveolar dehiscence. The first strategy (the binary classification method (BCM)) was to take the task directly as a classic binary classification. The second strategy (the key-point based method (KPM)) was to use the CNN to search two key points (i.e., the cement-enamel junction (CEJ) and the alveolar crest (AC)) firstly and then make a diagnosis according to the distance between the two key points. In both CNN strategies, 90% of the mandibular incisor images were assigned to the training dataset, and the rest 10% were assigned to the testing *** BCM showed limited performance in the diagnosis of alveolar dehiscence, with diagnostic accuracy below 70% for all the five classification networks. The KPM showed superior diagnostic performance with an accuracy of 90.9%, a sensitivity of 81.4% and a specificity of 94.4%, respectively, in the testing *** results proved that the diagnosis of alveolar dehiscence could be realized using CNN and the proposed KPM have the advantage of high accuracy and real-time performance. This study suggests that the KPM might have the potential for the diagnosis of alveolar dehiscence in the clinic. © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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GESH-NET: GRAPH-ENHANCED SPHERICAL HARMONIC CONVOLUTIONAL NETWORKS FOR CORTICAL SURFACE REGISTRATION

arXiv

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arXiv 2024年

作者： Zhang, Ruoyu Wang, Lihui Tang, Kun Xu, Jingwen Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Cortical surface registration plays a crucial role in coordinating individual cortical functions and anatomical features, serving as a fundamental step in cortical surface analysis. Its aim is to align the anatomical or functional regions of different individuals, which is of great importance for neuroimaging studies across different populations. Currently, cortical surface registration techniques based on classical methods have been well developed. However, a key issue with classical registration methods is that for each pair of images to be registered, it is necessary to search for the optimal transformation in the deformation space according to a specific optimization algorithm until the similarity measure function converges, which cannot meet the requirements of real-time and high-precision in medical image registration. With the spectacular success of deep learning in the field of computer vision, researching cortical surface image registration techniques based on deep learning models has become a new direction. But so far, there are still only a few studies on cortical surface image registration based on deep learning. Moreover, although deep learning methods theoretically have stronger representation capabilities, surpassing the most advanced classical methods in registration accuracy and distortion control remains a challenge. Therefore, to address this challenge, this paper constructs a deep learning model to study the technology of cortical surface image registration. The specific work is as follows: (1) An unsupervised cortical surface registration network based on a multi-scale cascaded structure is designed, and a convolution method based on spherical harmonic transformation is introduced to register cortical surface data. This solves the problem of scale-inflexibility of spherical feature transformation and optimizes the multi-scale registration process. The results show that the proposed network outperforms the other deep learning-based registration m

关键词： Spheres

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EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)

arXiv

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arXiv 2022年

作者： Wang, Haoran Zhu, Yan Qin, Wenzheng Zhang, Yizhe Zhou, Pinghong Li, Quanlin Wang, Shuo Song, Zhijian Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Endoscopy Center Endoscopy Research Institute Zhongshan Hospital Fudan University Shanghai200032 China Shanghai Collaborative Innovation Center of Endoscopy Shanghai200032 China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu210014 China

The advance of computer-aided detection systems using deep learning opened a new scope in endoscopic image analysis. However, the learning-based models developed on closed datasets are susceptible to unknown anomalies in complex clinical environments. In particular, the high false positive rate of polyp detection remains a major challenge in clinical practice. In this work, we release the FPPD-13 dataset, which provides a taxonomy and real-world cases of typical false positives during computer-aided polyp detection in real-world colonoscopy. We further propose a post-hoc module EndoBoost, which can be plugged into generic polyp detection models to filter out false positive predictions. This is realized by generative learning of the polyp manifold with normalizing flows and rejecting false positives through density estimation. Compared to supervised classification, this anomaly detection paradigm achieves better data efficiency and robustness in open-world settings. Extensive experiments demonstrate a promising false positive suppression in both retrospective and prospective validation. In addition, the released dataset can be used to perform ‘stress’ tests on established detection systems and encourages further research toward robust and reliable computer-aided endoscopic image analysis. The dataset and code will be publicly available at http://***. © 2022, CC BY.

关键词： Anomaly detection

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Combined Transcranial and Endoscopic Endonasal Approach for Resecting a Giant Intracranial and Extracranial Communicating Meningioma:A Case Report

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Clinical Cancer Bulletin 2022年第3期1卷 167-171页

作者： Ye GU Kai XUE Quan LIU Huankang ZHANG Hongmeng YU Department of Neurosurgery Eye&ENT HospitalFudan UniversityShanghaiChina Department of Otolaryngology Eye&ENT HospitalFudan UniversityShanghaiChina Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Fudan UniversityShanghaiChina Research Units of New Technologies of Endoscopic Surgery in Skull Base Tumor Chinese Academy of Medical SciencesShanghaiChina

Objective Although intracranial and extracranial communicating meningiomas are uncommon in clinical settings,they pose a considerable challenge for ***,one-stage surgery should be more comprehensively explained as it is not frequently reported as a first-line *** report A 27-year-old man with a massive intracranial and extracranial communicating meningioma was admitted with nasal congestion and mild numbness on the left side of his face.A combined transcranial and endoscopic endonasal approach was performed in a single day aided by preoperative tumor *** tumor was nearly completely removed without any serious ***-up revealed the disappearance of diplopia,with the patient having a normal ***,no tumor progression was *** The combined transcranial and endoscopic endonasal approach is feasible for removing certain massive intracranial and extracranial communicating *** techniques,such as embolization,microscopic,and endoscopic manipulation,are indispensable tools for treating such cases.

关键词： meningioma transcranial approach endoscopic endonasal approach

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Towards Disease-Aware Self-Supervised Dynamic Brain Network Learning For Mental Diagnosis

Towards Disease-Aware Self-Supervised Dynamic Brain Network ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhiyong Jin Guangqi Wen Peng Cao Lingwen Liu Jinzhu Yang Xinrong Zhu Osmar R. Zaiane Fei Wang Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Department of Psychiatry Affiliated Nanjing Brain Hospital Early Intervention Unit Nanjing Medical University Nanjing China Alberta Machine Intelligence Institute University of Alberta Edmonton Alberta Canada

The dynamic brain network learning methods ignored the separation of redundant disease-irrelevant information, resulting in the model only achieving suboptimal diagnosis results. Meanwhile, the supervised learning scheme inevitably suffers from poor generalization due to the limited data. To address these problems, we propose a Self-supervised Dynamic Brain network Disentangled representation learning framework named SDBD, which incorporates 1) a dynamic topology-aware encoder for capturing diverse topological information, 2) a cross decoder for reconstructing the graph structure and 3) a spatio-temporal learning model based on the multi-head self-attention mechanism for classification. To disentangle the disease-related information from the dynamic brain networks, we design a temporal contrastive loss and a structure reconstruction loss. We evaluate our model on three real-world mental diseases: Autism Spectrum Disorder (ASD), Major Depressive Disorder (MDD), and Bipolar Disorder (BD). The results indicate significant improvements in our SDBD over the state-of-the-art methods owing to the disentangled disease-related information. Moreover, our method can identify the biomarkers associated with the diseases, which is consistent with the previous studies. To the best of our knowledge, our work is the first attempt to disentangle the disease-related information for the dynamic brain network analysis. The code is available at https://***/IntelliDAL/Graph/tree/main/SDBD.

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2d-3d Hierarchical Feature Fusion Network For Segmentation Of Bone Structure In Knee Mr image

2d-3d Hierarchical Feature Fusion Network For Segmentation O...

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IEEE International Symposium on Biomedical Imaging

作者： Hui Wang Demin Yao Jiayi Chen Yanjing Liu Wensheng Li Yonghong Shi Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Fudan University Shanghai China. Histology and Embryology School of Basic Medical Sciences Fudan University Shanghai China.

ISBN: (纸本)9781665412469;9781665429474

Automatic segmentation of knee bone structures is an important task in orthopedics diagnosis of knee disease based on MRI images. Inspired by doctors' diagnosis of knee in sagittal plane of MR image, we propose to first calculate the sagittal maximum intensity projection (MIP) of MR image, then construct a high precision 2D-3D hierarchical feature fusion network for automatic segmentation of knee based on convolutional encoding and decoding architecture. It includes: 1) A 2D bypass network extracting global features based on MIP; 2) A 3D backbone network calculating local details based on MR volume; 3) A feature fusion module integrating 2D global context and 3D local details hierarchically. Particularly, the global features as anchor points will be fused with the local details at each level of the encoding path to enrich the context of local details and improve the segmentation accuracy. Our method is verified on SKI10 dataset. The average dice coefficients of femur, femoral cartilage, tibia and tibia cartilage are 0.978, 0.848, 0.979 and 0.848, respectively, and the segmentation performance is far better than the state-of-the-art methods.

关键词： image segmentation Three-dimensional displays image coding Magnetic resonance imaging Semantics medical services Feature extraction

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Deformable registration framework for glioma images with absent correspondence based on auxiliary-image-aided intensity-consistency constraint

Deformable registration framework for glioma images with abs...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kun Tang Lihui Wang Menglong Yang Jingwen Xu Xinyu Cheng Jian Zhang Yuemin Zhu Hongjiang Wei Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China INSA Lyon CNRS Inserm Univ Lyon Lyon France School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity.

关键词： image registration Pathology Deformation Magnetic resonance imaging Anatomical structure Bioinformatics Tumors Biomedical imaging

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Super Resolution of Pathology images with Hierarchical Feature Integration and Local image Patterns

SSRN

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SSRN 2024年

作者： Gao, Chaofei Xu, Feng Li, Lei Wang, Shuyang Ling, Ren Zhang, Xie Deng, Xi Zhou, Mengzhe Ling, Jin Department of Gastroenterology The Affiliated Lihuili Hospital of Ningbo University Zhejiang Ningbo315040 China Pathological Diagnosis Department Meikang Medicalsystem Laboratory. Ningbo Zhejiang 315105 China Department of Pathology School of Basic Medical Sciences Fudan University Xuhui District Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Xuhui District Shanghai200032 China Department of Research and Development Shanghai Laizi Software Technology Co. LTD. Fengxian District Shanghai201499 China Ningbo Clinical Pathology Diagnosis Center Zhejiang Ningbo315020 China MOE Key Laboratory of Bioinformatics Bioinformatics Division BNRist Department of Automation Tsinghua University Beijing100084 China

The rapid development of artificial intelligence and digital pathology has demonstrated promise in precision diagnosis in clinical. However, pathology analysis at single cell and sub-cellular level is still limited by the image resolution as the high resolution image acquirement is expensive and time-consuming. Additionally, most super resolution models ignored the intrinsic characteristics of pathology images without reaching satisfactory performance. In this paper, we developed a hierarchical super resolution deep learning framework based on local image patterns (HLIP) to reconstruct super resolution images from the low resolution ones with arbitrary input size and magnification. HLIP integrates hierarchical feature maps and conducts reconstruction by considering the local image patterns of neighboring pixels. Benchmark analysis shows HLIP achieves the best performance on both the internal and external validation datasets and the generated super resolution images approximate high resolution ones, which have reached clinical quality. It is revealed that HLIP can be applied in multiple clinical scenes as a versatile tool to improve the performance of other models on the tasks including the gland segmentation, cell segmentation and Helicobacter pylori detection. Moreover, HLIP can be integrated directly into the existing computer-aided pathology diagnostic systems for real-time super resolution. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Visual sensing approach for high-precision pressure estimation in robotic manipulation

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Engineering Applications of Artificial Intelligence 2025年 158卷

作者： Wensheng Wang Xueli Liu Linxin Bai Xiaobo Yang Xinrong Chen Academy for Engineering and Technology Fudan University Shanghai 200433 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai 200032 China Eye & ENT Hospital of Fudan University Shanghai 200031 China Department of Urology General Hospital of Ningxia Medical University Yinchuan 750000 China

Accurate sensing of contact pressure in robotic grippers is essential for both autonomous and teleoperated manipulation. However, the direct integration of tactile sensors onto gripper surfaces often poses significant challenges. With the advancement of high-resolution cameras and computer vision technologies, vision-based pressure estimation methods have emerged as viable alternatives. This paper presents a novel high-precision vision-based pressure estimation (HiVPE) method that addresses limitations in existing approaches, particularly when dealing with posture changes, low image quality, environmental variations, and occlusion. The key contributions of this work are in the realm of artificial intelligence (AI), where a novel dual-channel feature enhancement architecture is proposed, comprising a Spatial Feature Modulation (SFM) module for detailed manipulator-specific perception and a Visual Pressure Mamba (VP-Mamba) module based on state space models for capturing long-range dependencies and global structural information. In the engineering domain, the application of this AI technology involves implementing an eye-in-hand camera system to generate pressure maps from gripper images, enabling robust pressure estimation across diverse real-world scenarios without direct sensor integration. Experimental evaluations in both controlled laboratory settings and diverse real-world environments demonstrate excellent performance, with a temporal accuracy of 96.22% and 95.48% for tendon-actuated and soft grippers, respectively, while reducing the mean absolute error by 5.6% and 19.5% compared to baseline methods. These results validate the superior performance of HiVPE in complex operational tasks and unknown environments, making it a feasible solution for practical robotic applications.

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