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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical images, CARE 2024 Held in Conjunction with 27th International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Huang, Jinlong Sun, Xiao Wang, Lisheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031870088

Revolutionizing medical image analysis, the Segment Anything Model (SAM) stands out among emerging foundation models, yet its potential in accurately segmenting small, irregular ROIs from multi-center, diverse datasets remains untapped in real-world scenarios. Our study harnesses SAM’s capabilities to address the segmentation intricacies of clinically crucial, yet challenging, features in complex medical imaging. To bridge this gap, we embark on a comprehensive exploration anchored in Foundation models including SegVol and MedSAM, targeting three pivotal challenges: 1) enhancing the generalization of models across diverse medical image modalities and sequences, 2) reinforcing the stability of interactive segmentation, and 3) overcoming the segmentation hurdles posed by minuscule, atypical ROIs. We employ a coarse-to-fine segmentation strategy, using the CARE24 dataset alongside a meticulously publicly available dataset to fine-tune these models for enhanced real-world performance. To boost the stability and precision of interactive segmentation, we devise a retest mechanism, ensuring consistent outcomes in user-guided processes. Furthermore, recognizing the potential of multimodal data, we introduce a fusion strategy, capitalizing on complementary information to improve overall segmentation accuracy and adaptability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Complicated ROIs Foundation model Multi-center Segment Anything Model (SAM)

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ClusVPR: Efficient Visual Place recognition With Clustering-Based Weighted Transformer

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 1038-1049页

作者： Xu, Yifan Shamsolmoali, Pourya Zareapoor, Masoume Yang, Jie Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China East China Normal University School of Communication Shanghai200240 China University of York YorkYO10 5DD United Kingdom

Visual place recognition (VPR) is a highly challenging task that has a wide range of applications, including robot navigation and self-driving vehicles. VPR is a difficult task due to duplicate regions and insufficient attention to small objects in complex scenes, resulting in recognition deviations. In this article, we present ClusVPR, a novel approach that tackles the specific issues of redundant information in duplicate regions and representations of small objects. Different from existing methods that rely on convolutional neural networks (CNNs) for feature map generation, ClusVPR introduces a unique paradigm called clustering-based weighted transformer network (CWTNet). CWTNet uses the power of clustering-based weighted feature maps and integrates global dependencies to effectively address visual deviations encountered in large-scale VPR problems. We also introduce the optimized-VLAD (OptLAD) layer, which significantly reduces the number of parameters and enhances model efficiency. This layer is specifically designed to aggregate the information obtained from scale-wise image patches. Additionally, our pyramid self-supervised strategy focuses on extracting representative and diverse features from scale-wise image patches rather than from entire images. This approach is essential for capturing a broader range of information required for robust VPR. Extensive experiments on four VPR datasets show our model's superior performance compared to existing models while being less complex. © 2024 IEEE.

关键词： Self-supervised learning

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

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Proceedings of the 2025 5th International Conference on Bioinformatics and Intelligent Computing

作者： Ying Zhang Yuan Liu Xiaoyong Pan Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. The functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. Then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.

关键词： Binary PPI prediction

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Quaternion CNN With Salient Features for Color image Denoising

Quaternion CNN With Salient Features for Color Image Denoisi...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yi Liu Qiyu Jin Jie Yang School of Mathematical Science Inner Mongolia University Hohhot China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep convolutional neural networks have significantly advanced color image denoising. However, existing models often apply grayscale denoising techniques to color images without accounting for inter-channel correlations, resulting in color distortion, detail loss, and visual artifacts. Moreover, these models frequently neglect salient features within convolutional maps. To address these issues, we propose a quaternion CNN model that captures channel correlations and extracts salient features, thereby enhancing color image denoising performance. Specifically, we convert color images into quaternion matrices to better capture these correlations and design a quaternion convolutional network to learn relevant features. Furthermore, an aggregated feature block is introduced to enhance the extraction of salient features and further refine the denoising process. Experimental results on multiple datasets demonstrate that the proposed model achieves superior performance compared to recent state-of-the-art methods.

关键词： Visualization Correlation Acoustic distortion image color analysis Convolution Quaternions Noise reduction Color Feature extraction Convolutional neural networks

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Stock Investor Sentiment Analysis Based on NLP 1st

Stock Investor Sentiment Analysis Based on NLP

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1st International Conference on pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Liang, Jingxin Deng, Chao Yang, Jie Shanghai Normal University Tianhua College AI School Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

This study investigates the reciprocal relationship between investor sentiment and capital market dynamics, leveraging advanced techniques in data extraction and analysis. By employing web crawling and natural language processing (NLP), we gather and categorize investor comments from stock forums pertinent to the Shanghai and Shenzhen Indices. Utilizing Support Vector Machines and Logistic Regression within an NLP framework, we develop an automated system that transforms comments into sentiment indicators. These indicators are further quantified into daily sentiment indices following denoising procedures to emphasize persistent market trends. Comparative assessments reveal strong correlations between refined sentiment indices and actual stock index movements, confirming their predictive power for market fluctuations. The study offers investors a sentiment-analytics tool, augmenting the multi-faceted approach to investment strategy formulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Web crawler

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Application of Classification Framework Based on CDR and CNN in Ophthalmic Prediagnosis 1st

Application of Classification Framework Based on CDR and CNN...

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1st International Conference on pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Xu, Yunjiao Yang, Jie Guo, Tianjiao Scientific Research Office Shanghai Normal University Tianhua College Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

The seven-field classification plays a key role in ophthalmology, especially in the diagnosis and treatment of diabetic retinopathy. In this paper, we designed a framework that can classify 7 fields of each eye simultaneously, and applied it to the medical-assisted interactive system of 7 fields of classification。F1 and F2 field of view are detected through a combination of algorithms based on decision rules and convolutional neural networks. The total accuracy on the internal test set is 98.7%(95%CI, 98.4%–99.1%). In external tests, the total accuracy of the algorithm in this paper on the LONGITUDINAL data set is 98.6%(95%CI,97.9%–99.3%). The total accuracy on the Drishti-GS data set is 100%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units

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patterns 2025年

作者： Li, Yi-Fan Pan, Xiaoyong Shen, Hong-Bin 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 Shanghai200240 China

We describe NLSExplorer, an interpretable approach for nuclear localization signal (NLS) prediction. By utilizing the extracted information on nuclear-specific sites from the protein language model to assist in NLS detection, NLSExplorer achieves superior performance with greater than 10% improvement in the F1 score compared with existing methods on benchmark datasets and highlights other nuclear transport segments. We applied NLSExplorer to the nucleus-localized proteins in the Swiss-Prot database to extract valuable segments. A comprehensive analysis of these segments revealed a potential NLS landscape and uncovered features of nuclear transport segments across 416 species. This study introduces a powerful tool for exploring the NLS universe and provides a versatile network that can efficiently detect characteristic domains and motifs. © 2025 The Author(s)

关键词： Deep learning

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Family Doctor Model Training Based on Large Language Model Tuning 1st

Family Doctor Model Training Based on Large Language Model T...

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1st International Conference on pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Zhu, Yu Li, Wangtao Ling, Xufeng Yang, Jie Shanghai China School of AI Shanghai Normal University Tianhua College Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

As society increasingly focuses on health issues, the importance of the role of family doctors becomes more apparent. The question of how to use cutting-edge technology to improve the accessibility and efficiency of medical services is a critical topic. Generative large language models, with their excellent capabilities in natural language processing, offer new possibilities for the implementation of intelligent family doctor systems. This study focuses on the application of generative large language models in family doctor systems, selecting the ChatGLM2-6B model as the base model for this research. Through a custom-written data collection program, a dataset containing 170,000 high-quality medical question-and-answer entries was constructed. The LoRA method was used to tune the base model, and the performance of the original and tuned models in medical question-and-answer tasks was examined. The practical results of this study show that the generatively tuned large language model has achieved a 70% improvement in ROUGE scores compared to the untuned model, validating the effectiveness of large language model tuning in the family doctor field. Furthermore, this study has open-sourced the collected dataset and program, providing a technical reference for deploying efficient and accurate question-and-answer systems in similar vertical fields in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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Speckle reduction of SAR images using ICA basis enhancement and separation

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Chinese Optics Letters 2007年第9期5卷 509-512页

作者：李昱彤周越 Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University

An approach for synthetic aperture radar （SAR） image de-noising based on independent component analysis （ICA） basis images is proposed. Firstly, the basis images and the code matrix of the original image are obtained using ICA algorithm. Then, pointwise Hoder exponent of each basis is computed as a cost criterion for basis enhancement, and then the enhanced basis images are classified into two sets according to a separation rule which separates the clean basis from the original basis. After these key procedures for speckle reduction, the clean image is finally obtained by reconstruction on the clean basis and original code matrix. The reconstructed image shows better visual perception and image quality compared with those obtained by other traditional techniques.

关键词： Algorithms image quality Independent component analysis Speckle Synthetic aperture radar

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Two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation

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High Technology Letters 2014年第1期20卷 34-41页

作者：胡振涛 Liu Xianxing Li Jie Institute of Image Processing and Pattern Recognition Henan University

The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

关键词： multi-sensor information fusion particle filter weight optimization predictionand update

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