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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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Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation

arXiv

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arXiv 2023年

作者： Wang, Haonan Cao, Peng Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China DAMO Academy Alibaba Group Hangzhou China Amii University of Alberta Edmonton Canada

Most state-of-the-art methods for medical image segmentation adopt the encoder-decoder architecture. However, this U-shaped framework still has limitations in capturing the non-local multi-scale information with a simple skip connection. To solve the problem, we firstly explore the potential weakness of skip connections in U-Net on multiple segmentation tasks, and find that i) not all skip connections are useful, each skip connection has different contribution;ii) the optimal combinations of skip connections are different, relying on the specific datasets. Based on our findings, we propose a new segmentation framework, named UDTransNet, to solve three semantic gaps in U-Net. Specifically, we propose a Dual Attention Transformer (DAT) module for capturing the channel- and spatial-wise relationships to better fuse the encoder features, and a Decoder-guided Recalibration Attention (DRA) module for effectively connecting the DAT tokens and the decoder features to eliminate the inconsistency. Hence, both modules establish a learnable connection to solve the semantic gaps between the encoder and the decoder, which leads to a high-performance segmentation model for medical images. Comprehensive experimental results indicate that our UDTransNet produces higher evaluation scores and finer segmentation results with relatively fewer parameters over the state-of-the-art segmentation methods on different public datasets. Code: https://***/McGregorWwww/UDTransNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Decoding

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Graph Contrastive Learning with Progressive Augmentations 25

Graph Contrastive Learning with Progressive Augmentations

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Yuhai Zhao Yejiang Wang Zhengkui Wang Wen Shan Miaomiao Huang Xingwei Wang School of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang Liaoning China InfoComm Technology Cluster Singapore Institute of Technology Singapore Singapore Singapore University of Social Sciences Singapore Singapore School of Computer Science and Engineering Northeastern University Shenyang Liaoning China

ISBN: (纸本)9798400712456

To be still yet still moving. - Do Hyun ChoeGraph contrastive learning (GCL) has recently gained prominence in unsupervised graph representation learning. Traditional GCL approaches generally focus on creating a single contrastive view alongside the main graph view, targeting invariant representation learning in a static framework. Our study introduces a novel manner: despite using static graphs, we aim to learn invariant representations by generating a series of evolving contrastive views with temporal coherence and multi-viewpoint insights at various granularities. In this context, we propose the Progressive Augmentation framework for Graph Contrastive Learning (PaGCL). This framework advances beyond traditional methods by producing a sequence of augmented views, each evolving from the previous one, and assigning timestamps based on piecewise smoothness. This approach enables our model to more effectively extract invariant features from these dynamic views, capturing multi-grained structural and temporal information. Our experiments on diverse benchmark datasets demonstrate that PaGCL significantly outperforms current state-of-the-art methods.

关键词： graph representation learning}

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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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A Hybrid Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data*

A Hybrid Feature Selection Method Based on Binary Differenti...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Weidong Xie Wei Li Yushan Fang Yuhuan Chi Kun Yu Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang China Medicine and Bioinformation Engineering Northeastern University Shenyang China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Obtaining essential genes from microarray data that can diagnose diseases can be very useful for researchers to understand diseases and develop drugs. However, the high computational cost due to the “curse of dimensionality” and the high redundancy among features limit the application of evolutionary algorithms to the feature selection problem for high-dimensional data. This paper proposes a two-stage hybrid feature selection method to address this problem. In the first stage, a simple and efficient filtering me thod is used to initially filter redundant features, reduce the feature dimensionality, and reduce the search space of the evolutionary algorithm in the second stage. In the second stage, we propose an improved differential evolution algorithm. We redesign the binary quantization of the differential evolution algorithm for the characteristics of microarray data and improve the algorithm’s variation process to balance the algorithm’s search efficiency and accuracy. In addition, we define the redundancy of feature subsets and add it to the fitness function to reduce the redundancy of the final feature subsets. The proposed method is compared with classical feature selection methods and advanced hybrid feature selection methods on eight publicly available microarray data, and the effectiveness and advancement of the proposed method are demonstrated.

关键词： Drugs Correlation coefficient Quantization (signal) Filtering Redundancy Evolutionary computation Filtering algorithms

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A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization 37

A Single-Loop Accelerated Extra-Gradient Difference Algorith...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Liu, Yuanyuan Shang, Fanhua An, Weixin Liu, Junhao Liu, Hongying Lin, Zhouchen Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China School of Computer Science and Technology College of Intelligence and Computing Tianjin University China Medical College Tianjin University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Peng Cheng Laboratory China

ISBN: (纸本)9781713899921

In this paper, we propose a novel extra-gradient difference acceleration algorithm for solving constrained nonconvex-nonconcave (NC-NC) minimax problems. In particular, we design a new extra-gradient difference step to obtain an important quasi-cocoercivity property, which plays a key role to significantly improve the convergence rate in the constrained NC-NC setting without additional structural assumption. Then momentum acceleration is also introduced into our dual accelerating update step. Moreover, we prove that, to find an Ε-stationary point of the function f, the proposed algorithm attains the complexity O(Ε-2) in the constrained NC-NC setting, while the best-known complexity bound is Oe(Ε-4), where Oe(·) hides logarithmic factors compared to O(·). As the special cases of the constrained NC-NC setting, the proposed algorithm can also obtain the same complexity O(Ε-2) for both the nonconvex-concave (NC-C) and convex-nonconcave (C-NC) cases, while the best-known complexity bounds are Oe(Ε-2.5) for the NC-C case and Oe(Ε-4) for the C-NC case. For fair comparison with existing algorithms, we also analyze the complexity bound to find Ε-stationary point of the primal function φ for the constrained NC-C problem, which shows that our algorithm can improve the complexity bound from Oe(Ε-3) to O(Ε-2). To the best of our knowledge, this is the first time that the proposed algorithm improves the best-known complexity bounds from Oe(Ε-4) and Oe(Ε-3) to O(Ε-2) in both the NC-NC and NC-C settings. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Constrained optimization

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Few-Shot Small Vessel Segmentation Using a Detail-Preserving Network Enhanced by Discriminator

SSRN

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SSRN 2024年

作者： Huang, Yan Yang, Jinzhu Sun, Qi Yuan, Yuliang Hou, Yang Shang, Jin Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Computer Science and Engineering Northeastern University Liaoning Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang China Department of Radiology Shengjing Hospital of China Medical University Liaoning Shenyang China

Accurate segmentation of small vessels, such as coronary and pulmonary arteries, is crucial for early detection and treatment of vascular diseases. However, challenges persist due to the vessel's small size, complex structures, morphological variations, and limited annotated data. To address these challenges, we propose a detail-preserving network enhanced by a discriminator to improve the few-shot small vessel segmentation performance. The detail-preserving network constructs a complex module with multi-residual hybrid dilated convolution, which can enhance the network's receptive field while preserving the image's full detail features, enabling it to better capture the small vessel's structural features. Simultaneously, discriminator enhancement is incorporated into the training process through adversarial learning, effectively utilizing large amounts of unlabeled data to boost the generalization and robustness of the segmentation model. We validate the proposed method on in-house and public coronary artery datasets and public pulmonary artery datasets. Experimental results demonstrate that the proposed method significantly improves segmentation accuracy, particularly for small vessels. Compared with other state-of-the-art methods, the proposed method achieves higher accuracy, a lower false positive rate, and superior generalization capability, effectively assisting the clinical diagnosis of vessel diseases. © 2024, The Authors. All rights reserved.

关键词： Semi-supervised learning

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Virtually-Federated Scheduling of Parallel Real-Time Tasks 42

Virtually-Federated Scheduling of Parallel Real-Time Tasks

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Jiang, Xu Guan, Nan Liang, Haochun Tang, Yue Qiao, Lei Yi, Wang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University China City University of Hong Kong Hong Kong Beijing Institute of Control Engineering China Uppsala University Sweden

ISBN: (纸本)9781665428026

Federated scheduling is a promising approach to schedule parallel real-time tasks, where each task exclusively executes on a set of dedicated processors. However, federated scheduling suffers significant resource wasting since a task typically only uses part of the processing capacity allocated to it, while the unused part cannot be shared with other tasks. To solve this problem, we present a virtually-federated scheduling approach, which both enjoys the good analyzability of federated scheduling and allows tasks to efficiently share processors with others. The main idea is to construct virtual processors on physical processors, and let a task exclusively execute on a set of virtual processors. As a physical processor is shared by virtual processors, tasks effectively share processors with each other. On the other hand, as each task exclusively executes on its own virtual processor set, the good analyzability of federated scheduling can be carried into to our virtually-federated scheduling approach. We conduct comprehensive performance evaluation to compare our proposed approach with existing methods of different types. Experiment results show that our approach consistently outperforms existing methods to a considerable extent under a wide range of parameter settings. ©2021 IEEE

关键词： Scheduling

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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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Blockchain-based multi-role healthcare data sharing system 22

Blockchain-based multi-role healthcare data sharing system

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Yu, Yao Li, Qirui Zhang, Qieshi Hu, Wenjian Liu, Shumei Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China

ISBN: (纸本)9781728162676

Blockchain has unique advantages in data privacy protection and data integrity. We can solve many security problems, such as the single point of failure and data sharing in the current centralized system through blockchain approach. Existing studies have demonstrated that the application of blockchain in the medical system could improve the patient's medical experience. However, we found that the blockchain-based medical systems didn't consider the problems of long insurance claim cycle and complicated procedures. There are also very few healthcare systems that provide targeted sharing protocols for medical data and personal health data. In this paper, we propose a multi-role healthcare data sharing system framework based on blockchain. In this system, we reduce the storage cost through the collaborative storage of blockchain and IPFS. Then we design a smart contract on insurance to help patients achieve automatic insurance claim. In addition, we design two different sharing protocols to realize the fine management of personal data. The system analysis shows that our proposed blockchain-based multi-role healthcare data sharing system can effectively address the actual needs of users and has perfect performance in data storage, privacy protection, insurance claims and personal data management. © 2021 IEEE.

关键词： Blockchain

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