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pattern recognition and Machine Learning (PRML), IEEE International Conference on

作者： Ju Feng Xufeng Ling Yubo Wang Jie Yang School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Shanghai Acoustics Laboratory Chinese Academy of Sciences Shanghai China Institute of Image Processing and Pattern Recognition and Institute of Medical Robotics Shanghai Jiaotong University Shanghai China

Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classification and identification, this paper proposes a mushroom classification method based on residual networks. Firstly, a network architecture with multiple residual blocks is designed, and it is trained using an image dataset. Then, a transfer learning strategy is employed to initialize the network parameters from a pre-trained model, followed by fine-tuning to adapt to the mushroom classification task. Finally, multiple testing experiments are conducted to evaluate the effectiveness of the proposed method. The experimental results demonstrate excellent performance of the proposed method in mushroom classification tasks. Compared to traditional feature extraction methods, it can better capture the details and texture features of mushrooms, thereby improving classification accuracy. In conclusion, the mushroom classification method based on residual networks exhibits high accuracy and generalization capability. This method has potential applications in the field of mushroom classification, aiding in the better identification and differentiation of poisonous mushrooms, thereby protecting human health.

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Optimal Initialization Conditions Discovery to Improve Clustering Based image Segmentation

SSRN

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SSRN 2022年

作者： Khan, Zubair Khan, Tehreem Yang, Jie Tu, Enmei Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China University of Central Punjab Bahawalpur Pakistan

A B S T R A C TIn this paper, we propose a solution to the most important factor that deteriorates the performance of the clustering algorithms. We propose an efficient approach for discovering optimal initialization conditions for clustering-driven high-quality image segmentation (EAIS). The proposed approach solves the key clustering issue by uniquely utilizing image histograms to determine the optimal initialization conditions for pixel clusters (segmentation). The proposed approach comprises five modules, deep image reconstruction, intra-histogram peaks determination, inter-histogram peaks association, mutual consensus-oriented cluster seeds merging, and morphological reconstruction-driven spatial post-processing. Diverse experimental results on the BSDS500 benchmark validate that our proposed approach outperforms state-of-the-art (SOTA) methods regarding segmentation quality and computational efficiency. © 2022, The Authors. All rights reserved.

关键词： Merging

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CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection

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

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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

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.

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Discriminative Asymmetric Learning for Efficient Surgical Instrument Parsing

Discriminative Asymmetric Learning for Efficient Surgical In...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiaqi Liu Yu Qiao 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

Semantic segmentation of surgical instruments provides essential priors for autonomous surgery. This task is however challenging since the fine-structure of surgical instruments requires the accurate segmentation of detailed regions in images. As the visual guidance for autonomous surgery, the algorithm should also be real-time and friendly to embedded systems. In this paper, a discriminative asymmetric learning framework is proposed to balance the efficiency and effectiveness of surgical instrument segmentation. Two convolutional neural networks with specific designs are deployed to extract the detail and semantic features of instruments. To reduce the redundancy of visual representation, the aggregator-discriminator mechanism is proposed to distinguish the features learned from different levels. Experiments demonstrate that the proposed method contributes to competitive segmentation accuracy and a higher efficiency compared to existing methods.

关键词： image segmentation Visualization Embedded systems Instruments Semantics Redundancy Surgery

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Implications of neuroimaging findings in addiction

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Psychoradiology 2023年第1期3卷 18-23页

作者： Xinwen Wen Lirong Yue Zhe Du Linling Li Yuanqiang Zhu Dahua Yu Kai Yuan School of Life Science and Technology Xidian UniversityXi’an 710126China School of Biomedical Engineering Medical SchoolShenzhen UniversityShenzhen 518060China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen UniversityShenzhen 518060China Department of Radiology Xijing HospitalFourth Military Medical UniversityXi’an 710032China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information EngineeringInner Mongolia University of Science and TechnologyBaotou 014010China Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Xidian UniversityXi’an 710126China Xi’an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information School of Life Science and TechnologyXidian UniversityXi’an 710126China

Addiction is a chronic and often relapsing brain disorder characterized by drug abuse and withdrawal symptoms and compulsive drug seeking(Koob and Volkow,2010)when access to the drug is *** leads to structural and fun... 详细信息

关键词： motivation finding seeking

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Semi-Supervised Skin Lesion Segmentation with Learning Model Confidence

Semi-Supervised Skin Lesion Segmentation with Learning Model...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Zhiqiang Xie Enmei Tu Hao Zheng Yun Gu Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728176055;9781728176062

Segmentation of skin lesions is important for disease diagnoses and treatment planning. Over the years, semi-supervised methods using pseudo labels have boosted the segmentation performance with limited labeled data and abundant unlabeled data. However, the unreliable targets in pseudo labels might lead to meaningless guidance for unlabeled data. In this paper, to solve this issue, we propose a novel confidence aware semi-supervised learning method based on a mean teacher scheme. Concretely, we design a confidence module to predict the model confidence guided by the True Class Probability. Then in the mean teacher framework, the student model gradually learns trustworthy targets from teacher model. To further improve the segmentation quality, we fine-tune the student model with reliable content in pseudo labels. We conduct extensive experiments on 2018 ISIC skin lesion segmentation dataset and our method outperforms other state-of-the-art semi-supervised approaches.

关键词： Conferences Semisupervised learning Signal processing Predictive models Skin Planning Lesions

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BREAK: BRONCHI RECONSTRUCTION BY GEODESIC TRANSFORMATION AND SKELETON EMBEDDING

arXiv

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arXiv 2022年

作者： Yu, Weihao Zheng, Hao Zhang, Minghui Zhang, Hanxiao Sun, Jiayuan Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Department of Respiratory and Critical Care Medicine Department of Respiratory Endoscopy Shanghai Chest Hospital Shanghai Engineering Research Center of Respiratory Endoscopy China

Airway segmentation is critical for virtual bronchoscopy and computer-aided pulmonary disease analysis. In recent years, convolutional neural networks (CNNs) have been widely used to delineate the bronchial tree. However, the segmentation results of the CNN-based methods usually include many discontinuous branches, which need manual repair in clinical use. A major reason for the breakages is that the appearance of the airway wall can be affected by the lung disease as well as the adjacency of the vessels, while the network tends to overfit these special patterns in the training set. To learn robust features for these areas, we design a multi-stage framework that adopts the geodesic distance transform to capture the intensity changes between airway lumen and wall. Another reason for the breakages is the intra-class imbalance. Since the volume of the peripheral bronchi may be much smaller than the large branches in an input patch, the common segmentation loss is not sensitive to the breakages among the distal branches. Therefore, in this paper, a breakage-sensitive regularization term is designed and can be easily combined with other loss functions. Extensive experiments are conducted on publicly available datasets. Compared with state-of-the-art methods, our framework can detect more branches while maintaining competitive segmentation performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer aided analysis

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Diabetic Retinopathy Lesion Segmentation Using Deep Multi-Scale Framework

SSRN

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SSRN 2023年

作者： Guo, Tianjiao Yang, Jie Yu, Qi Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong Univeristy Shanghai China Department of Ophthalmology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China National Clinical Research Center for Eye Diseases Shanghai China

The segmentation of diabetic retinopathy (DR) lesions is important for large-scale screening using color fundus photography (CFP) images. The difficulty of this task is that the DR lesions have various sizes, shapes, and intensities. Traditional handcrafted feature-based approaches are unsatisfactory, and recent deep-learning-based approaches ignore the features of DR lesions. In this paper, we propose a segmentation approach that segments four typical DR lesions simultaneously based on convolutional neural networks (CNN). The raw CFP image is first pre-processed and resized to different sizes. A set of fully convolutional neural networks (FCNs) with various input sizes are then trained to extract the lesions from different scales. An auxiliary CNN is then introduced to fuse the output from these FCNs and refine the segmentation *** conducted our experiments on one local dataset and three public datasets including IDRiD, DDR, and DIARETDB1. The results of the area under the precision-recall-curve (AUPR) show that our approach achieves competitive *** improvement in performance indicates that this approach is beneficial to DR lesion segmentation and has potential in other segmentation tasks. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Stereo endoscopic image super-resolution using disparity-constrained parallel attention

arXiv

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arXiv 2020年

作者： Zhang, Tianyi Gu, Yun Huang, Xiaolin Tu, Enmei Yang, Jie Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

With the popularity of stereo cameras in computer assisted surgery techniques, a second viewpoint would provide additional information in surgery. However, how to effectively access and use stereo information for the super-resolution (SR) purpose is often a challenge. In this paper, we propose a disparity-constrained stereo super-resolution network (DCSSRnet) to simultaneously compute a super-resolved image in a stereo image pair. In particular, we incorporate a disparity-based constraint mechanism into the generation of SR images in a deep neural network framework with an additional atrous parallax-attention modules. Experiment results on laparoscopic images demonstrate that the proposed framework outperforms current SR methods on both quantitative and qualitative evaluations. Our DCSSRnet provides a promising solution on enhancing spatial resolution of stereo image pairs, which will be extremely beneficial for the endoscopic surgery. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Colonoscopic image Synthesis For Polyp Detector Enhancement Via Gan And Adversarial Training

Colonoscopic Image Synthesis For Polyp Detector Enhancement ...

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IEEE International Symposium on Biomedical Imaging

作者： Fan He Sizhe Chen Shuaiyi Li Lu Zhou Haiqin Zhang Haixia Peng Xiaolin Huang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Digestive Endoscopy Center Tongren Hospital Shanghai Jiao Tong University School of Medicine Institute of Medical Robotics Shanghai Jiao Tong University

Computer-aided polyp detection system powered by deep neural networks has achieved high performance but also suffers from data insufficiency. To address this problem, recent researches focus on synthesizing new colonoscopic images by Generative Adversarial Network (GAN). However, the synthesized images follow the same distribution as that of the training dataset, which limits the performance of the detectors re-trained on it. Recent studies show that adversarial examples can expand the data distribution and thus adversarial training can effectively improve the robustness of deep neural networks. Inspired by these two factors, this paper proposes a data augmentation framework to directly produce false negative colonoscopic images via GAN and the adversarial attack. The synthesized polyps are natural and experiments on three popular detectors show that compared with using GAN alone, producing false negative images by the adversarial attack can further improve the performance of the re-trained detectors.

关键词： Training image synthesis Neural networks Deep architecture Detectors Generative adversarial networks Robustness

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