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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

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Natural Image Matting Based on Surrogate Model

SSRN

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SSRN 2022年

作者： Liang, Yihui Gou, Hongshan Feng, Fujian Liu, Guisong Huang, Han School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu610000 China Laboratory of Intelligent Algorithms and Intelligent Software School of Software Engineering South China University of Technology Guangzhou510006 China

Image matting is important in digital image processing. The pixel pair optimization-based methods are some of the image matting methods that have distinct advantages in spatially disconnected foreground. However, they cannot provide high-quality alpha mattes with limited computing resources which limits their computing resource-sensitive applications. To address this problem, we present a natural image matting method based on a surrogate model. The surrogate model for pixel pair optimization is established in the presented method to approximate pixel pair fitness, and its analytical optimal solution is used as the approximate optimal solution of pixel pair optimization problem, which saves a lot of computing resources. Experimental results show that the image matting method based on a surrogate model provides high-quality matting mattes with low computing resources and its image matting performance is competitive against state-of-the-art pixel pair optimization-based methods which require a lot of computational resources. © 2022, The Authors. All rights reserved.

关键词： Pixels

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Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation

arXiv

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arXiv 2023年

作者： Zhao, Gangming Liang, Kongming Pan, Chengwei Zhang, Fandong Wu, Xianpeng Hu, Xinyang Yu, Yizhou The Department of Computer Science The University of Hong Kong Hong Kong Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Institute of Artificial Intelligence Beihang University Beijing China The AI Lab Deepwise Healthcare Beijing China Department of Cardiology of the Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Key Laboratory of Cardiovascular of Zhejiang Province Hangzhou China

Vessel segmentation is widely used to help with vascular disease diagnosis. Vessels reconstructed using existing methods are often not sufficiently accurate to meet clinical use standards. This is because 3D vessel structures are highly complicated and exhibit unique characteristics, including sparsity and anisotropy. In this paper, we propose a novel hybrid deep neural network for vessel segmentation. Our network consists of two cascaded subnetworks performing initial and refined segmentation respectively. The second subnetwork further has two tightly coupled components, a traditional CNN-based U-Net and a graph U-Net. Cross-network multi-scale feature fusion is performed between these two U-shaped networks to effectively support high-quality vessel segmentation. The entire cascaded network can be trained from end to end. The graph in the second subnetwork is constructed according to a vessel probability map as well as appearance and semantic similarities in the original CT volume. To tackle the challenges caused by the sparsity and anisotropy of vessels, a higher percentage of graph nodes are distributed in areas that potentially contain vessels while a higher percentage of edges follow the orientation of potential nearby vessels. Extensive experiments demonstrate our deep network achieves state-of-the-art 3D vessel segmentation performance on multiple public and in-house datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Anisotropy

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Stable and Feature Decoupled of Deep Mutual Information Maximization Based on Wasserstein Distance

SSRN

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SSRN 2023年

作者： He, Xing Peng, Changgen Wang, Lin Tan, Weijie State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University PR Guiyang550025 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University PR Guiyang550025 China Guizhou Big Data Academy Guizhou University PR Guiyang550025 China Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University PR Guiyang550025 China

Deep learning techniques usually require large amounts of expensive labeled data to train networks, and the extracted deep representations are usually mixed with multiple attributes having uninterpretability, which limits the application and development of deep learning techniques. Therefore, it is crucial to investigate the stability of training models by unsupervised learning methods and to learn feature decoupled representations. Although the method based on depth mutual information maximization(Deep InfoMax or DIM) has achieved good results in the field of computer vision, it is extremely prone to "gradient explosion" or "gradient disappearance", which makes the training model unstable. At the same time, it is difficult to decouple the features learned by the encoder. In order to solve these problems, we stable and feature decoupled of deep mutual information maximization based on Wasserstein distance, which we call the method called WDIM. The method first addresses the instability of the model trained by the DIM method and proposes to measure the distance between the encoder output and the prior distribution based on the Wasserstein distance, which is a good guide to the stability of the model training. Second, to address the problem that the features learned by the encoder are difficult to be decoupled, the mutual information between the features learned by the hidden layer of the encoder and the intermediate layer is required to be minimized during the training of the encoder so that the features learned by each filter are as uncorrelated as possible to achieve feature decoupling. Finally, the WDIM method is validated on the CIFAR-10, CIFAR-100, STL-10, and FashionMNIST datasets. Experiments show that our proposed WDIM method is more stable for model training, faster model convergence, and more significant feature decoupling. © 2023, The Authors. All rights reserved.

关键词： Network coding

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xin Xu Wenjing Lu Jiahao Lei Peng Qiu Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Department of Vascular Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods.

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Making a bird AI expert work for you and me

arXiv

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arXiv 2021年

作者： Chang, Dongliang Pang, Kaiyue Du, Ruoyi Ma, Zhanyu Song, Yi-Zhe Guo, Jun The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China SketchX CVSSP University of Surrey Guildford GU2 7XH United Kingdom

As powerful as fine-grained visual classification (FGVC) is, responding your query with a bird name of "Whip-poor-will" or "Mallard" probably does not make much sense. This however commonly accepted in the literature, underlines a fundamental question interfacing AI and human - what constitutes transferable knowledge for human to learn from AI? This paper sets out to answer this very question using FGVC as a test bed. Specifically, we envisage a scenario where a trained FGVC model (the AI expert) functions as a knowledge provider in enabling average people (you and me) to become better domain experts ourselves, i.e. those capable in distinguishing between "Whip-poor-will" and "Mallard". Fig. 1 lays out our approach in answering this question. Assuming an AI expert trained using expert human labels, we ask (i) what is the best transferable knowledge we can extract from AI, and (ii) what is the most practical means to measure the gains in expertise given that knowledge? On the former, we propose to represent knowledge as highly discriminative visual regions that are expert-exclusive. For that, we devise a multi-stage learning framework, which starts with modelling visual attention of domain experts and novices before discriminatively distilling their differences to acquire the expert exclusive knowledge. For the latter, we simulate the evaluation process as book guide to best accommodate the learning practice of what is accustomed to humans. A comprehensive human study of 15,000 trials shows our method is able to consistently improve people of divergent bird expertise to recognise once unrecognisable birds. Interestingly, our approach also leads to improved conventional FGVC performance when the extracted knowledge defined is utilised as means to achieve discriminative localisation. Codes are available at: https://***/PRIS-CV/Making-a-BirdAI-Expert-Work-for-You-and-Me. © 2021, CC BY.

关键词： Birds

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Comparison of Backbones for Semantic Segmentation Network 5

Comparison of Backbones for Semantic Segmentation Network

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2020 5th International Conference on intelligent Computing and Signal Processing, ICSP 2020

作者： Zhang, Rongyu Du, Lixuan Xiao, Qi Liu, Jiaming International School Beijing University of Post and Telecommunication Beijing100876 China International School of BusinessandFiance Sun Yat-Sen University Guangzhou510275 China Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing100876 China

As for the classification network that is constantly emerging with each passing day, different classification network as the backbone of the semantic segmentation network may show different performance. This paper selected the road extraction data set of CVPR DeepGlobe, and compared the performance differences of VGG-16 as the backbone of Unet, ResNet34, ResNet101 and Xception as the backbone of AD-LinkNet. When VGG-16 is used as the backbone of the semantic segmentation network, it performs better in the face of long and wide road extraction. As the backbone of the semantic segmentation network, ResNet has a higher ability to extract small roads. When Xception is used as the backbone of the semantic segmentation network, it not only retains the characteristics of ResNet34, but also can effectively deal with the complex situation of extracting target covered by occlusions. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

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Tracking system for driving assistance with the faster R-CNN 2nd

Tracking system for driving assistance with the faster R-CNN

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2nd International Conference on Computer, Communication and Computational Sciences, IC4S 2017

作者： Yang, Kai Zhang, Chuang Wu, Ming Pattern Recognition and Intelligent System Lab School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811303432

The vehicle detection and tracking in driving assistance system are ordinarily achieved by the optical or radar technology. In this work, we explore video processing for driving assistance system. An object’s detection and tracking system based on the Faster R-CNN and Camshift algorithm is proposed, and Kalman filtering algorithm is used to predict the position of objects. Our system differs with other object-tracking algorithms based on deep learning, which directly detects each frame and has no tracking part, such as YOLO and SSD. We also introduce an evaluation criterion for driving assistance system according to the dangerous level of the surrounding cars. Through the experiments on the videos recorded by drive recorder, we show that our approach can achieve better performance in complex scenes than YOLO or SSD and can satisfy the real-time requirement by processing 30 frames per second. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Object detection

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Towards transferable adversarial attack against deep face recognition

arXiv

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arXiv 2020年

作者： Zhong, Yaoyao Deng, Weihong Pattern Recognition and Intelligent System Laboratory School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

—Face recognition has achieved great success in the last five years due to the development of deep learning methods. However, deep convolutional neural networks (DCNNs) have been found to be vulnerable to adversarial examples. In particular, the existence of transferable adversarial examples could severely hinder the robustness of DCNNs since this type of attacks could be applied in a fully black-box manner without queries on the target system. In this work, we first investigate the characteristics of transferable adversarial attacks in face recognition by showing the superiority of feature-level methods over label-level methods. Then, to further improve transferability of feature-level adversarial examples, we propose DFANet, a dropout-based method used in convolutional layers, which could increase the diversity of surrogate models and obtain ensemble-like effects. Extensive experiments on state-of-the-art face models with various training databases, loss functions and network architectures show that the proposed method can significantly enhance the transferability of existing attack methods. Finally, by applying DFANet to the LFW database, we generate a new set of adversarial face pairs that can successfully attack four commercial APIs without any queries. This TALFW database is available to facilitate research on the robustness and defense of deep face recognition. Copyright © 2020, The Authors. All rights reserved.

关键词： Face recognition

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PropagationNet: propagate points to curve to learn structure information

arXiv

引用

arXiv 2020年

作者： Huang, Xiehe Deng, Weihong Shen, Haifeng Zhang, Xiubao Ye, Jieping Pattern Recognition & Intelligent System Laboratory School of Information and Communication Engineering Beijing University of Posts and Telecommunications AI Labs DiDi Chuxing

Deep learning technique has dramatically boosted the performance of face alignment algorithms. However, due to large variability and lack of samples, the alignment problem in unconstrained situations, e.g. large head poses, exaggerated expression, and uneven illumination, is still largely unsolved. In this paper, we explore the instincts and reasons behind our two proposals, i.e. Propagation Module and Focal Wing Loss, to tackle the problem. Concretely, we present a novel structure-infused face alignment algorithm based on heatmap regression via propagating landmark heatmaps to boundary heatmaps, which provide structure information for further attention map generation. Moreover, we propose a Focal Wing Loss for mining and emphasizing the difficult samples under in-the-wild condition. In addition, we adopt methods like CoordConv and Anti-aliased CNN from other fields that address the shift-variance problem of CNN for face alignment. When implementing extensive experiments on different benchmarks, i.e. WFLW, 300W, and COFW, our method outperforms state-of-the-arts by a significant margin. Our proposed approach achieves 4.05% mean error on WFLW, 2.93% mean error on 300W full-set, and 3.71% mean error on COFW. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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