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Collaborative learning of graph generation, clustering and classification for brain networks diagnosis

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022年 219卷 106772-106772页

作者： Yang, Wenju Wen, Guangqi Cao, Peng Yang, Jinzhu Zaiane, Osmar R. College of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Alberta Machine Intelligence Institute University of Alberta Edmonton Canada

Purpose: Accurate diagnosis of autism spectrum disorder (ASD) plays a key role in improving the condition and quality of life for patients. In this study, we mainly focus on ASD diagnosis with functional brain networks (FBNs). The major challenge for brain networks modeling is the high dimensional connectivity in brain networks and limited number of subjects, which hinders the classification capability of graph convolutional networks (GCNs). Method: To alleviate the influence of the limited data and high dimensional connectivity, we introduce a unified three-stage graph learning framework for brain network classification, involving multi-graph clustering, graph generation and graph classification. The framework combining Graph Generation, Clustering and Classification Networks (GraphCGC-Net) enhances the critical connections by multi-graph clustering (MGC) with a supervision scheme, and generates realistic brain networks by simultaneously preserving the global consistent distribution and local topology properties. Results: To demonstrate the effectiveness of our approach, we evaluate the performance of the proposed method on the Autism Brain Imaging Data Exchange (ABIDE) dataset and conduct extensive experiments on the ASD classification problem. Our proposed method achieves an average accuracy of 70.45% and an AUC of 72.76% on ABIDE. Compared with the traditional GCN model, the proposed GraphCGC-Net obtains 9.3%, and 10.64% improvement in terms of accuracy and AUC metrics, respectively. Conclusion: The comprehensive experiments demonstrate that our GraphCGC-Net is effective for graph classification in brain disorders diagnosis. Moreover, we find that MGC can generate biologically meaningful subnetworks, which is highly consistent with the previous neuroimaging-derived biomarker evidence of ASD. More importantly, the promising results suggest that applying generative adversarial networks (GANs) in brain networks to improve the classification performance is worth fur

关键词： Autism spectrum disorder Functional brain networks Multi-graph clustering Generative adversarial networks Graph convolutional networks

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Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation

arXiv

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arXiv 2023年

作者： Wang, Changqi Xie, Haoyu Yuan, Yuhui Fu, Chong Yue, Xiangyu School of Computer Science and Engineering Northeastern University Shenyang China The Chinese University of Hong Kong Hong Kong Microsoft Research Asia China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education NEU China

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. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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FAST: a dual-tier few-shot learning paradigm for whole slide image classification 24

FAST: a dual-tier few-shot learning paradigm for whole slide...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Kexue Fu Xiaoyuan Luo Linhao Qu Shuo Wang Ying Xiong Ilias Maglogiannis Longxiang Gao Manning Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Fudan University University of Piraeus

ISBN: (纸本)9798331314385

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST.

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GESH-NET: GRAPH-ENHANCED SPHERICAL HARMONIC CONVOLUTIONAL NETWORKS FOR CORTICAL SURFACE REGISTRATION

arXiv

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arXiv 2024年

作者： Zhang, Ruoyu Wang, Lihui Tang, Kun Xu, Jingwen Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Cortical surface registration plays a crucial role in coordinating individual cortical functions and anatomical features, serving as a fundamental step in cortical surface analysis. Its aim is to align the anatomical or functional regions of different individuals, which is of great importance for neuroimaging studies across different populations. Currently, cortical surface registration techniques based on classical methods have been well developed. However, a key issue with classical registration methods is that for each pair of images to be registered, it is necessary to search for the optimal transformation in the deformation space according to a specific optimization algorithm until the similarity measure function converges, which cannot meet the requirements of real-time and high-precision in medical image registration. With the spectacular success of deep learning in the field of computer vision, researching cortical surface image registration techniques based on deep learning models has become a new direction. But so far, there are still only a few studies on cortical surface image registration based on deep learning. Moreover, although deep learning methods theoretically have stronger representation capabilities, surpassing the most advanced classical methods in registration accuracy and distortion control remains a challenge. Therefore, to address this challenge, this paper constructs a deep learning model to study the technology of cortical surface image registration. The specific work is as follows: (1) An unsupervised cortical surface registration network based on a multi-scale cascaded structure is designed, and a convolution method based on spherical harmonic transformation is introduced to register cortical surface data. This solves the problem of scale-inflexibility of spherical feature transformation and optimizes the multi-scale registration process. The results show that the proposed network outperforms the other deep learning-based registration m

关键词： Spheres

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Sub-kHz-Linewidth and Wide-Tuning External Cavity Laser Based on Low-Loss Lithium Niobate Photonics

Sub-kHz-Linewidth and Wide-Tuning External Cavity Laser Base...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Ge, Renyou Chen, Bigeng Yu, Shaoliang Luo, Yannong Gao, Shengqian Research Center for Optical Fiber Sensing Zhejiang Lab Hangzhou311100 China Research Center for Intelligent Optoelectronic Computing Zhejiang Lab Hangzhou311100 China Life Science Institute Guangxi Medical University Nanning530021 China State Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Guangzhou510006 China

We demonstrate a sub-kHz-linewidth laser on LNOI platform utilizing long delay and narrowband filtering. Lasing with high side mode suppression ratio above 60dB, wide tuning range of 80nm, and narrow linewidth of 270H... 详细信息

ISBN: (纸本)9781957171395

关键词： Linewidth

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Sub-kHz-Linewidth and Wide-Tuning External Cavity Laser Based on Low-Loss Lithium Niobate Photonics

Sub-kHz-Linewidth and Wide-Tuning External Cavity Laser Base...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Ge, Renyou Chen, Bigeng Yu, Shaoliang Luo, Yannong Gao, Shengqian Research Center for Optical Fiber Sensing Zhejiang Lab Hangzhou311100 China Research Center for Intelligent Optoelectronic Computing Zhejiang Lab Hangzhou311100 China Life Science Institute Guangxi Medical University Nanning530021 China State Key Laboratory of Optoelectronic Materials and technologies Sun Yat-sen University Guangzhou510006 China

关键词： Linewidth

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Hexagonal Convolutional Neural Network for Spatial Transcriptomics Classification

Hexagonal Convolutional Neural Network for Spatial Transcrip...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Gao, Jing Zhang, Fa Hu, Kai Cui, Xuefeng Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou China Shandong University School of Computer Science and Technology Qingdao China

ISBN: (纸本)9781665468190

Recent advances in spatial transcriptomics have enabled the comprehensive measurement of transcriptional profiles while retaining the spatial contextual information. Identifying spatial domains is a critical step in the analysis of spatially resolved transcriptomics. Existing unsupervised methods perform poorly on this task owing to the large amount of noise and dropout events in the transcriptomic profiles. To address this problem, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Second, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent nodes. Compared with the graph convolution in graph neural networks (GNNs), our hexagonal convolution can preserve the relative spatial location information of different nodes in graph-structured data. Third, based on the hexagonal convolution, a novel hexagonal Convolutional Neural Network (hexCNN) is proposed for spatial transcriptomics classification. Finally, we compared the proposed hexCNN with existing methods on the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 87.2% and an average Rand index (ARI) of 78.2% (1.9% and 3.3% higher than those of GNNs). © 2022 IEEE.

关键词： Convolution

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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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Medka: A Knowledge Graph-Augmented Approach to Improve Factuality in medical Large Language Models

SSRN

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SSRN 2025年

作者： Deng, Yiyan Zhao, Shen Miao, Yongming Zhu, Junjie Li, Jin Department of Artificial Intelligence Nanjing University of Information Science and Technology Jiangsu Nanjing China Department of Breast Surgery Fudan University Shanghai Cancer Center Shanghai China Department of Oncology Shanghai Medical College Fudan University Shanghai China Jiangsu Key Laboratory of Intelligent Medical Image Computing Jiangsu Nanjing China

Large language models (LLMs) have demonstrated remarkable potential in medical applications. However, they still face critical challenges such as hallucinations, knowledge inconsistency, and insufficient integration of domain-specific medical expertise. To address these limitations, we introduce MedKA, a novel knowledge graph-augmented approach for fine-tuning and evaluating medical LLMs. Our approach transforms structured knowledge from the knowledge graph into high-quality corpus, the cMKGQA dataset, which is used for one-stage LLM fine-tuning. This transformation process ensures the comprehensive integration of domain-specific knowledge while maintaining factual consistency. To evaluate the effectiveness of our approach, we introduce the Knowledge Graph-based Auxiliary Evaluation Metrics (KG-AEMs), a novel framework specifically designed to assess the factual accuracy of medical responses. Experimental results demonstrate that MedKA achieves state-of-the-art performance, significantly outperforming existing models, including LLaMA-3.1-8B-Chinese-Chat, HuatuoGPT2-7B, and Apollo2-7B. On the cMKGQA dataset, MedKA achieves 44.63 BLEU-1 and 17.62 BLEU-4 scores, with particularly strong performance in areas such as medication recommendations and diagnostic tests as measured by KG-AEMs. Our approach highlights the potential of integrating knowledge graphs into LLM fine-tuning to improve the accuracy and reliability of medical AI systems. It advances factual accuracy in medical dialogue systems and provides a comprehensive framework for evaluating the integration of medical knowledge into LLMs. This work is publicly available on Github: https://***/Yai017/MedKA. © 2025, The Authors. All rights reserved.

关键词： Knowledge graph

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Encoding Temporal Change of Longitudinal EBV DNA for Dynamic Risk Prediction of Nasopharyngeal Carcinoma 7

Encoding Temporal Change of Longitudinal EBV DNA for Dynamic...

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7th International Conference on Biological Information and Biomedical Engineering, BIBE 2024

作者： Yang, Fangfang Zhen, Zicheng Lin, Li Liu, Yuyu Gao, Rui Zhang, Taoyu Lu, Lijun Lv, Wenbing School of Information Yunnan University Yunnan Kunming650504 China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Yunnan Kunming650504 China Department of Radiation Oncology Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou510060 China School of Software Yunnan University Yunnan Kunming650504 China School of Biomedical Engineering Southern Medical University 1023 Shatai Road Guangzhou510515 China

ISBN: (纸本)9783800762910

—To accurately predict the risk of recurrence and metastasis of nasopharyngeal carcinoma (NPC) patients, an image-encoded risk prediction model was proposed. 752 patients (training vs. testing: 527 vs. 225) were retrospectively collected, and the plasma EBV DNA copies were tested 13 times during treatment. Based on 13 times plasma EBV DNA copies, three kinds of image encoding methods were employed to characterize the linear and nonlinear dynamic change of longitudinal EBV DNA, i.e. Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Recurrence Plot (RP). The Cox proportional hazard regression model was constructed by incorporating both image and clinical features as covariates to determine the risk factors and prediction models for disease-free survival (DFS). On the test set, the proposed model combining image and clinical features achieved C-index and AUC of 0.777 and 0.776, respectively outperforming the model relying solely on clinical features (C-index = 0.667, AUC = 0.675). In conclusion, the dynamic risk prediction model with image encoding can monitor changes in plasma EBV DNA copies during treatment, aiding doctors in assessing whole-body tumor burden, accurately predicting the dynamic risk of recurrence and metastasis, and planning individualized treatments in NPC patients. © VDE VERLAG GMBH.

关键词： Gene encoding

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