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CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection

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arXiv 2023年

作者： Xu, Jiasheng Zhang, Tianyi Wu, Yangqian Yang, Jie Yang, Guang-Zhong Gu, Yun The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China The Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery. In this work, a cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens, as well as to provide artifact cues to characterize the visual features. To effectively extract the structural and artifact features, the Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Furthermore, we design a Guided Feature Fusion (GFF) module to supervise the model for adaptive feature fusion based on different types of artifacts. Results show that the features extracted by the proposed method can preserve the structural information of lumen in the presence of large visual variations, bringing much-improved lumen detection accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature extraction

Hybrid Data-Free Knowledge Distillation

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arXiv 2024年

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://***/tangjialiang97/HiDFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection

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CDFI: Cross Domain Feature Interaction for Robust Bronchi Lu...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiasheng Xu Tianyi Zhang Yangqian Wu Jie Yang Guang–Zhong Yang Yun Gu Institute of Medical Robotics Shanghai Jiao Tong University Shanghai CHINA Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai CHINA MOE Key Laboratory of System Control and Information Processing Shanghai P.R. China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

关键词：

Modeling Inter-Intra Heterogeneity for Graph Federated Learning

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arXiv 2024年

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Code is available at https://***/blgpb/FedIIH. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation

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arXiv 2023年

作者： Tang, Fenghe Nian, Bingkun Ding, Jianrui Quan, Quan Yang, Jie Liu, Wei Zhou, S. Kevin School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China School of Computer Science and Technology Harbin Institute of Technology China Institute of Computing Technology China

Due to the scarcity and specific imaging characteristics in medical images, light-weighting Vision Transformers (ViTs) for efficient medical image segmentation is a significant challenge, and current studies have not yet paid attention to this issue. This work revisits the relationship between CNNs and Transformers in lightweight universal networks for medical image segmentation, aiming to integrate the advantages of both worlds at the infrastructure design level. In order to leverage the inductive bias inherent in CNNs, we abstract a Transformer-like lightweight CNNs block (ConvUtr) as the patch embeddings of ViTs, feeding Transformer with denoised, non-redundant and highly condensed semantic information. Moreover, an adaptive Local-Global-Local (LGL) block is introduced to facilitate efficient local-to-global information flow exchange, maximizing Transformer's global context information extraction capabilities. Finally, we build an efficient medical image segmentation model (MobileUtr) based on CNN and Transformer. Extensive experiments on five public medical image datasets with three different modalities demonstrate the superiority of MobileUtr over the state-of-the-art methods, while boasting lighter weights and lower computational cost. Code is available at https://***/FengheTan9/MobileUtr. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

MambaMIM: Pre-training Mamba with State Space Token-interpolation

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arXiv 2024年

作者： Tang, Fenghe Nian, Bingkun Li, Yingtai Yang, Jie Wei, Liu Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

Generative self-supervised learning demonstrates outstanding representation learning capabilities in both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). However, there are currently no generative pre-training methods related to selective state space models (Mamba) that can handle long-range dependencies effectively. To address this challenge, we introduce a generative self-supervised learning method for Mamba (MambaMIM) based on Selective Structure State Space Sequence Token-interpolation (S6T), a general-purpose pre-training method for arbitrary Mamba architectures. Our method, MambaMIM, incorporates a bottom-up 3D hybrid masking strategy in the encoder to maintain masking consistency across different architectures. Additionally, S6T is employed to learn causal relationships between the masked sequence in the state space. MambaMIM can be used on any single or hybrid Mamba architectures to enhance the Mamba long-range representation capability. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for pretraining medical image tasks. The code is available at: https://***/FengheTan9/MambaMIM. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution

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arXiv 2023年

作者： Nian, Bingkun Tang, Fenghe Ding, Jianrui Zhang, Pingping Yang, Jie Kevin Zhou, S. Liu, Wei Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China China School of Computer Science and Technology Harbin Institute of Technology China School of artificial intelligence Dalian University of Technology China

In this paper, we present a high-performance deep neural network for weak target image segmentation, including medical image segmentation and infrared image segmentation. To this end, this work analyzes the existing dynamic convolutions and proposes dynamic parameter convolution (DPConv). Furthermore, it reevaluates the relationship between reconstruction tasks and segmentation tasks from the perspective of DPConv, leading to the proposal of a dual-network model called the Siamese Reconstruction-Segmentation Network (SRSNet). The proposed model is not only a universal network but also enhances the segmentation performance without altering its structure, leveraging the reconstruction task. Additionally, as the amount of training data for the reconstruction network increases, the performance of the segmentation network also improves synchronously. On seven datasets including five medical datasets and two infrared image datasets, our SRSNet consistently achieves the best segmentation results. The code is released at https://***/fidshu/SRSNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

Vehicle Routing Problem with Time Windows using Hybrid Metaheuristic Dragonfly Algorithm and Variable Neighborhood Search: Work on Progress

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Vehicle Routing Problem with Time Windows using Hybrid Metah...

International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Yunita Yunita Deris Stiawan Dian Palupi Rini Doctoral Student at Faculty of Computer Science Sriwijaya University Intelligent System Research Group Faculty of Computer Science Sriwijaya University Palembang South Sumatera Communication Network and Information Security Research Group Faculty of Computer Science Sriwijaya University Palembang South Sumatera Image Processing dan Pattern Recognition Laboratory Group Faculty of Computer Science Sriwijaya University Palembang South Sumatera

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

One of the problems with Smart Transportation is the problem of cost and travel time. This problem is known as the Variable Routing Problem (VRP). In some real cases, in addition to considering route selection, there are limitations on the capacity and time period for the vehicle to serve each customer known as Time Windows, so VRP has developed into Variable Routing Problem with Time Windows (VRPTW). One way to solve VRPTW is to use metaheuristic methods. However, the metaheuristic method has a weakness, which are it can get stuck in local optimums and fail to find better global solutions. To overcome this, a combination with other methods or known as hybrid is needed. The formulation of this research problem is how to develop a Smart transportation model using a metaheuristic hybrid algorithm. The purpose of this study is to develop a Smart transportation model using a metaheuristic hybrid algorithm. The method used is to combine two metaheuristic algorithms, which are the Dragonfly Algorithm (DA) and the Variable Neighborhood Search (VNS). The result of this research is a new algorithm model which is a hybrid between DA and VNS. Through a combination of global exploration using DA and local exploitation using VNS, this algorithm is expected to be able to find a better solution and faster convergence in solving VRPTW problems

关键词： Electrical engineering Computer science Costs Metaheuristics Vehicle routing Routing Search problems Smart transportation Informatics Convergence

Universal Adversarial Perturbation Generated by Attacking Layer-wise Relevance Propagation 10

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Universal Adversarial Perturbation Generated by Attacking La...

10th IEEE International Conference on Intelligent Systems, IS 2020

作者： Wang, Zifei Huang, Xiaolin Yang, Jie Kasabov, Nikola Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland New Zealand

ISBN: (纸本)9781728154565

The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has become an important research area of machine learning. It has been known that many state-of-the-art DNNs suffer the risk of universal adversarial perturbations, which are image-agnostic and able to lead misclassifications with high probability. In this paper, we propose a novel method to create such universal adversarial perturbations. Our approach is the first to generate universal perturbations by attacking the attention heat maps with the interpretation method, Layer-wise Relevance Propagation. It is demonstrated that our method achieves high fooling ratios on image classification DNNs pre-trained by imageNet dataset. Moreover, our attack shows good transferability across different DNNs. © 2020 IEEE.

关键词： Deep neural networks