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AtlasSeg: Atlas Prior Guided Dual-U-Net for Tissue Segmentation in Fetal Brain MRI

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arXiv 2024年

作者： Xu, Haoan Zheng, Tianshu Xu, Xinyi Shen, Yao Sun, Jiwei Sun, Cong Wang, Guangbin Cui, Zhaopeng Wu, Dan Key Laboratory for Biomedical Engineering of Ministry of Education Department of Biomedical Engineering College of Biomedical Engineering & Instrument Science Zhejiang University Hangzhou310027 China Department of Radiology Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Chinese Academy of Medical Sciences Beijing100730 China Department of Radiology Shandong Provincial Hospital Affiliated to Shandong First Medical University Shandong Jinan276899 China State Key Lab of CAD&CG Zhejiang University Hangzhou China

Objective: Accurate automatic tissue segmentation in fetal brain MRI is a crucial step in clinical diagnosis but remains challenging, particularly due to the dynamically changing anatomy and tissue contrast during fetal development. Existing segmentation networks can only implicitly learn age-related features, leading to a decline in accuracy at extreme early or late gestational ages (GAs). Methods: To improve segmentation performance throughout gestation, we introduce AtlasSeg, a dual-U-shape convolution network that explicitly integrates GA-specific information as guidance. By providing a publicly available fetal brain atlas with segmentation labels corresponding to relevant GAs, AtlasSeg effectively extracts age-specific patterns in the atlas branch and generates precise tissue segmentation in the segmentation branch. Multi-scale spatial attention feature fusions are constructed during both encoding and decoding stages to enhance feature flow and facilitate better information interactions between two branches. Results: We compared AtlasSeg with six well-established networks in a seven-tissue segmentation task, achieving the highest average Dice similarity coefficient of 0.91. The improvement was particularly evident in extreme early or late GA cases, where training data was scare. Furthermore, AtlasSeg exhibited minimal performance degradation on low-quality images with contrast changes and noise, attributed to its anatomical shape priors. Conclusion: AtlasSeg demonstrated enhanced segmentation accuracy, better consistency across fetal ages, and robustness to perturbations, making it a powerful tool for reliable fetal brain MRI tissue segmentation, particularly suited for diagnostic assessments during early gestation. Copyright © 2024, The Authors. All rights reserved.

关键词： Tissue

OpenSlot: Mixed Open-Set Recognition with Object-Centric Learning

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arXiv 2024年

作者： Yin, Xu Pan, Fei An, Guoyuan Huo, Yuchi Xie, Zixuan Yoon, Sung-Eui School of Computing Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of School of Computer Science Engineering University of Michigan United States State Key Lab of CAD and CG Zhejiang University China Zhejiang Lab 310058 China Institute of Computing Technology University of Chinese Academy of Sciences Beijing100190 China Faculty of School of Computing Korea Advanced Institute of Science and Technology Deajeon34141 Korea Republic of

Existing open-set recognition (OSR) studies typically assume that each image contains only one class label, with the unknown test set (negative) having a disjoint label space from the known test set (positive), a scenario referred to as full-label shift. This paper introduces the mixed OSR problem, where test images contain multiple class semantics, with both known and unknown classes co-occurring in the negatives, leading to a more complex super-label shift that better reflects real-world scenarios. To tackle this challenge, we propose the OpenSlot framework, based on object-centric learning, which uses slot features to represent diverse class semantics and generate class predictions. The proposed anti-noise slot (ANS) technique helps mitigate the impact of noise (invalid or background) slots during classification training, addressing the semantic misalignment between class predictions and ground truth. We evaluate OpenSlot on both mixed and conventional OSR benchmarks. Without elaborate designs, our method not only excels existing approaches in detecting super-label shifts across OSR tasks, but also achieves state-of-the-art performance on conventional benchmarks. Meanwhile, OpenSlot can localize class objects without using bounding boxes during training, demonstrating competitive performance in open-set object detection and potential for generalization. © 2024, CC BY-NC-ND.

关键词： Object detection

An Improved EfficientNetV2 Model Based on Visual Attention Mechanism: Application to Identification of Cassava Disease

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Computational Intelligence and Neuroscience 2022年第0期2022卷 1569911页

作者： Ye, Yuanbo Zhou, Houkui Yu, Huimin Hu, Haoji Zhang, Guangqun Hu, Junguo He, Tao School of Mathematics and Computer Science Zhejiang A and F University Hangzhou311300 China Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of CAD and CG Hangzhou310027 China

With the characteristic of high recognition rate and strong network robustness, convolutional neural network has now become the most mainstream method in the field of crop disease recognition. Aiming at the problems with insufficient numbers of labeled samples, complex backgrounds of sample images, and difficult extraction of useful feature information, a novel algorithm is proposed in this study based on attention mechanisms and convolutional neural networks for cassava leaf recognition. Specifically, a combined data augmentation strategy for datasets is used to prevent single distribution of image datasets, and then the PDRNet (plant disease recognition network) combining channel attention mechanism and spatial attention mechanism is proposed. The algorithm is designed as follows. Firstly, an attention module embedded in the network layer is deployed to establish remote dependence on each feature layer, strengthen the key feature information, and suppress the interference feature information, such as background noise. Secondly, a stochastic depth learning strategy is formulated to accelerate the training and inference of the network. And finally, a transfer learning method is adopted to load the pretrained weights into the model proposed in this study, with the recognition accuracy of the model enhanced by means of detailed parameter adjustments and dynamic changes in the learning rate. A large number of comparative experiments demonstrate that the proposed algorithm can deliver a recognition accuracy of 99.56% on the cassava disease image dataset, reaching the state-of-the-art level among CNN-based methods in terms of accuracy. © 2022 Yuanbo Ye et al.

关键词： Convolutional neural networks

Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions

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Learning Causal Representation for Training Cross-Domain Pos...

International Conference on computer Vision (ICCV)

作者： Xiheng Zhang Yongkang Wong Xiaofei Wu Juwei Lu Mohan Kankanhalli Xiangdong Li Weidong Geng State Key Laboratory of CAD&CG College of Computer Science and Technology Zhejiang University School of Computing National University of Singapore Huawei Noah’s Ark Laboratory

ISBN: (纸本)9781665428132

3D pose estimation has attracted increasing attention with the availability of high-quality benchmark datasets. However, prior works show that deep learning models tend to learn spurious correlations, which fail to generalize beyond the specific dataset they are trained on. In this work, we take a step towards training robust models for cross-domain pose estimation task, which brings together ideas from causal representation learning and generative adversarial networks. Specifically, this paper introduces a novel framework for causal representation learning which explicitly exploits the causal structure of the task. We consider changing domain as interventions on images under the data-generation process and steer the generative model to produce counterfactual features. This help the model learn transferable and causal relations across different domains. Our framework is able to learn with various types of unlabeled datasets. We demonstrate the efficacy of our proposed method on both human and hand pose estimation task. The experiment results show the proposed approach achieves state-of-the-art performance on most datasets for both domain adaptation and domain generalization settings.

关键词： Training Representation learning Deep learning Visualization Three-dimensional displays Correlation Pose estimation

MetaGlyph: Automatic Generation of Metaphoric Glyph-based Visualization

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arXiv 2022年

作者： Ying, Lu Shu, Xinhuan Deng, Dazhen Yang, Yuchen Tang, Tan Yu, Lingyun Wu, Yingcai The State Key Lab CAD&CG Zhejiang University Hangzhou China The Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong School of Art and Archaeology Zhejiang University Hangzhou China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China

Glyph-based visualization achieves an impressive graphic design when associated with comprehensive visual metaphors, which help audiences effectively grasp the conveyed information through revealing data semantics. However, creating such metaphoric glyph-based visualization (MGV) is not an easy task, as it requires not only a deep understanding of data but also professional design skills. This paper proposes MetaGlyph, an automatic system for generating MGVs from a spreadsheet. To develop MetaGlyph, we first conduct a qualitative analysis to understand the design of current MGVs from the perspectives of metaphor embodiment and glyph design. Based on the results, we introduce a novel framework for generating MGVs by metaphoric image selection and an MGV construction. Specifically, MetaGlyph automatically selects metaphors with corresponding images from online resources based on the input data semantics. We then integrate a Monte Carlo tree search algorithm that explores the design of an MGV by associating visual elements with data dimensions given the data importance, semantic relevance, and glyph non-overlap. The system also provides editing feedback that allows users to customize the MGVs according to their design preferences. We demonstrate the use of MetaGlyph through a set of examples, one usage scenario, and validate its effectiveness through a series of expert interviews. © 2022, CC BY.

关键词： Visualization

Benchmarking Neural Radiance Fields for Autonomous Robots: An Overview

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SSRN

SSRN 2024年

作者： Ming, Yuhang Yang, Xingrui Wang, Weihan Chen, Zheng Feng, Jinglun Xing, Yifan Zhang, Guofeng School of Computer Science Hangzhou Dianzi University Hangzhou310018 China High-speed Aerodynamics Institute CARDC Mianyang621000 China Stevens Institute of Technology HobokenNJ07030 United States Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47405 United States City College of New York New YorkNY10031 United States School of Computer Science University of Bristol BristolBS8 1UB United Kingdom State Key Lab of CAD&CG Zhejiang University Hangzhou310058 China

Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene representation, offering high-fidelity renderings and reconstructions from a set of sparse and unstructured sensor data. In the context of autonomous robotics, where perception and understanding of the environment are pivotal, NeRF holds immense promise for improving performance. In this paper, we present a comprehensive survey and analysis of the state-of-the-art techniques for utilizing NeRF to enhance the capabilities of autonomous robots. We especially focus on the perception, localization and navigation, and decision-making modules of autonomous robots and delve into tasks crucial for autonomous operation, including 3-dimensional reconstruction, segmentation, pose estimation, simultaneous localization and mapping, navigation and planning, and interaction. Our survey meticulously benchmarks existing NeRF-based methods, providing insights into their strengths and limitations. Moreover, we explore promising avenues for future research and development in this domain. Notably, we discuss the integration of advanced techniques such as 3-dimensional Gaussian splatting, large language models, and generative artificial intelligence, envisioning enhanced reconstruction efficiency, scene understanding, decision-making capabilities. This survey serves as a roadmap for researchers seeking to leverage NeRF to empower autonomous robots, paving the way for innovative solutions that can navigate and interact seamlessly in complex environments. © 2024, The Authors. All rights reserved.

关键词： Decision making

Benchmarking Neural Radiance Fields for Autonomous Robots: An Overview

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arXiv 2024年

Neural Radiance Fields (NeRF) have emerged as a powerful paradigm for 3D scene representation, offering high-fidelity renderings and reconstructions from a set of sparse and unstructured sensor data. In the context of autonomous robotics, where perception and understanding of the environment are pivotal, NeRF holds immense promise for improving performance. In this paper, we present a comprehensive survey and analysis of the state-of-the-art techniques for utilizing NeRF to enhance the capabilities of autonomous robots. We especially focus on the perception, localization and navigation, and decision-making modules of autonomous robots and delve into tasks crucial for autonomous operation, including 3D reconstruction, segmentation, pose estimation, simultaneous localization and mapping (SLAM), navigation and planning, and interaction. Our survey meticulously benchmarks existing NeRF-based methods, providing insights into their strengths and limitations. Moreover, we explore promising avenues for future research and development in this domain. Notably, we discuss the integration of advanced techniques such as 3D Gaussian splatting (3DGS), large language models (LLM), and generative AIs, envisioning enhanced reconstruction efficiency, scene understanding, decision-making capabilities. This survey serves as a roadmap for researchers seeking to leverage NeRFs to empower autonomous robots, paving the way for innovative solutions that can navigate and interact seamlessly in complex environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

EventAnchor: Reducing human interactions in event annotation of racket sports videos

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arXiv 2021年

作者： Deng, Dazhen Wu, Jiang Wang, Jiachen Wu, Yihong Xie, Xiao Zhou, Zheng Zhang, Hui Zhang, Xiaolong Wu, Yingcai The State Key Lab of CAD&CG Zhejiang University HangzhouZhejiang China Department of Sport Science Zhejiang University HangzhouZhejiang China College of Information Sciences and Technology Pennsylvania State University United States

The popularity of racket sports (e.g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance. While sports videos offer many benefits for such analysis, retrieving accurate information from sports videos could be challenging. In this paper, we propose EventAnchor, a data analysis framework to facilitate interactive annotation of racket sports video with the support of computer vision algorithms. Our approach uses machine learning models in computer vision to help users acquire essential events from videos (e.g., serve, the ball bouncing on the court) and offers users a set of interactive tools for data annotation. An evaluation study on a table tennis annotation system built on this framework shows significant improvement of user performances in simple annotation tasks on objects of interest and complex annotation tasks requiring domain knowledge. © 2021, CC BY.

关键词： Sports

MaCAR: Urban traffic light control via active multi-agent communication and action rectification 29

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MaCAR: Urban traffic light control via active multi-agent co...

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Yu, Zhengxu Liang, Shuxian Wei, Long Jin, Zhongming Huang, Jianqiang Cai, Deng He, Xiaofei Hua, Xian-Sheng State Key Lab CAD&CG Zhejiang University Hangzhou China Computer Science Department Zhejiang University Hangzhou China DAMO Academy Alibaba Group Hangzhou China Fabu Inc. Hangzhou China

ISBN: (纸本)9780999241165

Urban traffic light control is an important and challenging real-world problem. By regarding intersections as agents, most of the Reinforcement Learning (RL) based methods generate actions of agents independently. They can cause action conflict and result in overflow or road resource waste in adjacent intersections. Recently, some collaborative methods have alleviated the above problems by extending the observable surroundings of agents, which can be considered as inactive cross-agent communication methods. However, when agents act synchronously in these works, the perceived action value is biased and the information exchanged is insufficient. In this work, we propose a novel Multi-agent Communication and Action Rectification (MaCAR) framework. It enables active communication between agents by considering the impact of synchronous actions of agents. MaCAR consists of two parts: (1) an active Communication Agent Network (CAN) involving a Message Propagation Graph Neural Network (MPGNN);(2) a Traffic Forecasting Network (TFN) which learns to predict the traffic after agents' synchronous actions and the corresponding action values. By using predicted information, we mitigate the action value bias during training to help rectify agents' future actions. In experiments, we show that our proposal can outperforms state-of-the-art methods on both synthetic and real-world datasets. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Reinforcement learning