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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Kai Liang, Wei Cao, Peng Yang, Jinzhu Li, Weiping Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China School of Software and Microelectronics Peking University Beijing China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

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A Pulmonary Artery-Vein Separation algorithm based on the relationship between sub-trees 2020

A Pulmonary Artery-Vein Separation algorithm based on the re...

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The Fourth International Symposium on image computing and Digital Medicine

作者： Wenjun Tan Key Laboratory of Intelligent Computing in Medical Image Ministry of Education China

ISBN: (纸本)9781450389686

This article presents a combined algorithm of pulmonary artery-vein (A/V) separation considering both global and local information, including: the transformation of geometric graph, sub-tree separation, and A/V classification. During the process, geometric graphs are firstly built based on the centerlines; A separation of the adhesion points is performed by applying the information stored in the data structure of the nodes and links in the geometric graphs; Sub-trees are then combined according to the local and global information; The combined sub-trees are growing into vascular trees using 2D area growth method; And finally, classification of the arterial and venous vascular trees based on the knowledge of anatomy. An average accuracy of 85% has been achieved of the overall process with a processing time range between 40 s to 50 s, which indicates good performance and stability of this algorithm.

关键词： algorithm Artery-Vein Separation

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Joint Hand-Object 3D Reconstruction From Monocular image Based On Fused Visual Cues And Pose Prior

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IEEE Transactions on Consumer Electronics 2025年

作者： Liu, Yawen Zhang, Xinkang Chen, Xinrong Fudan University Academy for Engineering and Technology 200433 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention 200032 China

Joint reconstruction of hands and manipulated objects from a monocular image is important perception technique which has recently achieved impressive progress. Compared to reconstructing hands and objects from temporal sequences, hand and object reconstruction from a single monocular image does not require continuous image frames and can be performed with just one camera, which is applicable to a wider range of scenarios. In existing methods, signed distance field is widely used due to its excellent geometric representation ability. However, visual cues and pose prior information are not fully explored in signed distance field estimation. Therefore, this paper proposes a new hand-object reconstruction framework to fuse implicit representations with more representative prior information, in which other prior information and visual cues are fully explored. First, when signed distance value of a 3D point is queried, both global features and local features from 2D image plane transformed from the 3D space are integrated. Second, the anchor-based mechanism is introduced into the hand keypoints prediction, which provides a more accurate pose prior. In the comparison with the state-of-the-art model, the experiment results on common benchmarks demonstrated that the proposed method had good performance and achieved a shape error about 0.106 cm2 on Obman and 0.412 cm2 on DexYCB. Especially on the Obman dataset, the hand chamfer distance CDh reduces 5.4%, the object chamfer distance CDo reduces 27.1%, the hand keypoints error Eh reduces 5.7% and the object center error Eo reduces 9.0%. © 1975-2011 IEEE.

关键词： Surface reconstruction

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Rethinking Multiple Instance Learning: Developing an Instance-Level Classifier via Weakly-Supervised Self-Training

arXiv

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arXiv 2024年

作者： Ma, Yingfan Luo, Xiaoyuan Yuan, Mingzhi Chen, Xinrong Wang, Manning The Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China The Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

Multiple instance learning (MIL) problem is currently solved from either bag-classification or instance-classification perspective, both of which ignore important information contained in some instances and result in limited performance. For example, existing methods often face difficulty in learning hard positive instances. In this paper, we formulate MIL as a semi-supervised instance classification problem, so that all the labeled and unlabeled instances can be fully utilized to train a better classifier. The difficulty in this formulation is that all the labeled instances are negative in MIL, and traditional self-training techniques used in semi-supervised learning tend to degenerate in generating pseudo labels for the unlabeled instances in this scenario. To resolve this problem, we propose a weakly-supervised self-training method, in which we utilize the positive bag labels to construct a global constraint and a local constraint on the pseudo labels to prevent them from degenerating and force the classifier to learn hard positive instances. It is worth noting that easy positive instances are instances are far from the decision boundary in the classification process, while hard positive instances are those close to the decision boundary. Through iterative optimization, the pseudo labels can gradually approach the true labels. Extensive experiments on two MNIST synthetic datasets, five traditional MIL benchmark datasets and two histopathology whole slide image datasets show that our method achieved new SOTA performance on all of them. The code will be publicly available. Impact Statement-MIL is a primary research field within weakly supervised learning and has numerous applications, such as histopathology analysis, video anomaly detection, and drug design. However, current mainstream methods in MIL predominantly rely on bag labels to train the network, consequently overlooking the potential of instance-level supervision information. In our paper, we highlight the

关键词： Self-supervised learning

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RKSeg+: make full use of Runge–Kutta methods in medical image segmentation

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Multimedia Systems 2024年第2期30卷 65-65页

作者： Zhu, Mai Fu, Chong Wang, Xingwei School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

The dynamical system perspective has been used to build efficient image classification networks and semantic segmentation networks. Furthermore, the Runge–Kutta (RK) methods are powerful tools for building networks from the dynamical systems perspective. Hence, the Runge–Kutta segmentation network (RKSeg) for medical image segmentation was born. Skip connections and multiple scaling are often used in common models but lack mathematical explanations. RKSeg interprets and uses skip connections based on the RK methods. Therefore, RKSeg greatly improves segmentation efficiency. However, it does not explain and use multiple scales from a dynamical system perspective but only inherits the multi-scale scheme of existing models. We compensate for this shortcoming by interpreting and using multiple scales based on the RK methods. In addition, the network structure also limits the excellent image classification networks as the backbones of RKSegs. Therefore, we modify the network structure to support more image classification networks as backbones. As a result, we propose a novel network structure RKSeg+. Our proposed RKSeg+ achieves better segmentation results with fewer parameters than RKSeg. Furthermore, RKSeg+, well configured with few parameters, outperforms state-of-the-art models on six of the ten organ datasets in the medical Segmentation Decathlon. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

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Ddfp: Data-Dependent Frequency Prompt for Source Free Domain Adaptation of medical image Segmentation

SSRN

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SSRN 2024年

作者： Yin, Siqi Liu, Shaolei Wang, Manning 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

Domain adaptation aims to address model performance degradation problem under domain gap. In the typical setting of unsupervised domain adaptation, labeled data from source domain and unlabeled data from target domain are used to train a target model. However, access to source domain medical data can be restricted due to privacy policies, in which scenario, the research attention has shifted to source free domain adaptation (SFDA) which only requires a trained source domain model and unlabeled target domain data for adaptation. Existing SFDA methods often utilize domain-dependent image style translation and self-supervision techniques to reduce domain gap and train the target domain model. However, the quality of domain-dependent style translated images and pseudo labels still leaves room for improvement. Moreover, training the entire model during adaptation is suboptimal under limited supervision. In this paper, we propose a novel SFDA framework to overcome these limitations. Specifically, to effectively mitigate the impact of domain gap in the initial training phase, we introduce pre-adaptation to derive a pre-adapted model for target model initialization and generate quality enhanced pseudo labels without introducing additional parameters. Furthermore, we propose a data-dependent frequency prompt to better translate target domain images into a source-like style individually. Subsequently, the style-related layers fine-tuning strategy tailored for SFDA is used to train the target model using the prompted target domain images and pseudo labels. Extensive experiments on cross-modality abdominal and cardiac SFDA segmentation tasks demonstrated that our proposed method outperforms the state-of-the-art baselines. Our code is available online. © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Rethinking Barely-Supervised Volumetric medical image Segmentation from an Unsupervised Domain Adaptation Perspective

arXiv

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arXiv 2024年

作者： Shen, Zhiqiang Cao, Peng Su, Junming Yang, Jinzhu Zaiane, Osmar R. School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Alberta Machine Intelligence Institute University of Alberta Edmonton Canada

This paper investigates an extremely challenging problem: barely-supervised volumetric medical image segmentation (BSS). A BSS training dataset consists of two parts: 1) a barely-annotated labeled set, where each labeled image contains only a single-slice annotation, and 2) an unlabeled set comprising numerous unlabeled volumetric images. State-of-the-art BSS methods employ a registration-based paradigm, which uses inter-slice image registration to propagate single-slice annotations into volumetric pseudo labels, constructing a completely annotated labeled set, to which a semi-supervised segmentation scheme can be applied. However, the paradigm has a critical limitation: the pseudo-labels generated by image registration are unreliable and noisy. Motivated by this, we propose a new perspective: instead of solving BSS within a semi-supervised learning scheme, this work formulates BSS as an unsupervised domain adaptation problem. To this end, we propose a novel BSS framework, Barely-supervised learning via unsupervised domain Adaptation (BvA), as an alternative to the dominant registration paradigm. Specifically, we first design a novel noise-free labeled data construction algorithm (NFC) for slice-to-volume labeled data synthesis. Then, we introduce a frequency and spatial Mix-Up strategy (FSX) to mitigate the domain shifts. Extensive experiments demonstrate that our method provides a promising alternative for BSS. Remarkably, the proposed method, trained on the left atrial segmentation dataset with only one barely-labeled image, achieves a Dice score of 81.20%, outperforming the state-of-the-art by 61.71%. The code is available at https://***/Senyh/BvA. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation

Space Engage: Collaborative Space Supervision for Contrastiv...

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International Conference on Computer Vision (ICCV)

作者： Changqi Wang Haoyu Xie Yuhui Yuan Chong Fu Xiangyu Yue School of Computer Science and Engineering Northeastern University Shenyang China Microsoft Research Asia Key Laboratory of Intelligent Computing in Medical Image Ministry of Education NEU China The Chinese University of Hong Kong

Semi-Supervised Semantic Segmentation (S4) aims to train a segmentation model with limited labeled images and a substantial volume of unlabeled images. To improve the robustness of representations, powerful methods introduce a pixel-wise contrastive learning approach in latent space (i.e., representation space) that aggregates the representations to their prototypes in a fully supervised manner. However, previous contrastive-based S4 methods merely rely on the supervision from the model’s output (logits) in logit space during unlabeled training. In contrast, we utilize the outputs in both logit space and representation space to obtain supervision in a collaborative way. The supervision from two spaces plays two roles: 1) reduces the risk of over-fitting to incorrect semantic information in logits with the help of representations; 2) enhances the knowledge exchange between the two spaces. Furthermore, unlike previous approaches, we use the similarity between representations and prototypes as a new indicator to tilt training those under-performing representations and achieve a more efficient contrastive learning process. Results on two public benchmarks demonstrate the competitive performance of our method compared with state-of-the-art methods.

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GALOPA: graph transport learning with optimal plan alignment 23

GALOPA: graph transport learning with optimal plan alignment

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yejiang Wang Yuhai Zhao Zhengkui Wang Ling Li School of Computer Science and Engineering Northeastern University China and Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China InfoComm Technology Cluster Singapore Institute of Technology Singapore

Self-supervised learning on graphs aims to learn graph representations in an unsupervised manner. While graph contrastive learning (GCL - relying on graph augmentation for creating perturbation views of anchor graphs and maximizing/minimizing similarity for positive/negative pairs) is a popular self-supervised method, it faces challenges in finding label-invariant augmented graphs and determining the exact extent of similarity between sample pairs to be achieved. In this work, we propose an alternative self-supervised solution that (i) goes beyond the label invariance assumption without distinguishing between positive/negative samples, (ii) can calibrate the encoder for preserving not only the structural information inside the graph, but the matching information between different graphs, (iii) learns isometric embeddings that preserve the distance between graphs, a by-product of our objective. Motivated by optimal transport theory, this scheme relies on an observation that the optimal transport plans between node representations at the output space, which measure the matching probability between two distributions, should be consistent with the plans between the corresponding graphs at the input space. The experimental findings include: (i) The plan alignment strategy significantly outperforms the counterpart using the transport distance; (ii) The proposed model shows superior performance using only node attributes as calibration signals, without relying on edge information; (iii) Our model maintains robust results even under high perturbation rates; (iv) Extensive experiments on various benchmarks validate the effectiveness of the proposed method.

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Review on application progress of federated learning model and security hazard protection

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Digital Communications and Networks 2023年第1期9卷 146-158页

作者： Aimin Yang Zezhong Ma Chunying Zhang Yang Han Zhibin Hu Wei Zhang Xiangdong Huang Yafeng Wu Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina College of Science North China University of Science and TechnologyTangshanHebeiChina College of Artificial Intelligence North China University of Science and TechnologyTangshanHebeiChina

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data *** data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data *** is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our *** the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied *** paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

关键词： Data silos Machine learning Federated learning Privacy protection Learning framework

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