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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Weibin Wang Fang Wang Yunjun Yang Yinhao Li Jing Liu Xianhua Han Lanfen Lin Ruofeng Tong Hongjie Hu Yen-Wei Chen Graduate School of Information Science and Engineering Ritsumeikan University Kusatsu Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University Hangzhou China Department of Radiology The First Affiliated Hospital Wenzhou Medical University Wenzhou China College of Information Science and Engineering Ritsumeikan University Kusatsu Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781665492331

Risk prediction for recurrence is a critical task for patients with hepatocellular carcinoma (HCC). Effective prediction can evaluate treatment options and guide personalized medicine. Traditional approaches use clinical data to construct cox-regression models to predict the risk of recurrence. Recently, prognosis analysis based on image information of patient often has better performance compared with traditional approaches. In particular, deep learning has demonstrated its superiority in medical image processing. In this paper, we propose a deep learning-based model to predict the risk of recurrence. We collected 292 patients with HCC from two independent centers, which were divided into a training set, an internal validation set, and an external validation set. Our models were compared with traditional clinical models and better performance was obtained. Our proposed method achieves a C-index performance of 0.627 and 0.630 for the internal validation set and external validation set, respectively.

关键词： Training Deep learning Precision medicine Computed tomography Predictive models data models Biomedical image processing

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Improving Transformers with Probabilistic Attention Keys

arXiv

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arXiv 2021年

作者： Nguyen, Tam Nguyen, Tan M. Le, Dung D. Nguyen, Duy Khuong Tran, Viet-Anh Baraniuk, Richard G. Ho, Nhat Osher, Stanley J. FPT Software AI Center Hanoi Viet Nam Department of Mathematics University of California Los AngelesCA United States College of Engineering and Computer Science VinUniversity Hanoi Viet Nam Deezer Research France Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Statistics and Data Sciences The University of Texas AustinTX United States

Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkable performance across a variety of natural language processing (NLP) and computer vision tasks. It has been observed that for many applications, those attention heads learn redundant embedding, and most of them can be removed without degrading the performance of the model. Inspired by this observation, we propose Transformer with a Mixture of Gaussian Keys (Transformer-MGK), a novel transformer architecture that replaces redundant heads in transformers with a mixture of keys at each head. These mixtures of keys follow a Gaussian mixture model and allow each attention head to focus on different parts of the input sequence efficiently. Compared to its conventional transformer counterpart, Transformer-MGK accelerates training and inference, has fewer parameters, and requires fewer FLOPs to compute while achieving comparable or better accuracy across tasks. Transformer-MGK can also be easily extended to use with linear attention. We empirically demonstrate the advantage of Transformer-MGK in a range of practical applications, including language modeling and tasks that involve very long sequences. On the Wikitext-103 and Long Range Arena benchmark, Transformer-MGKs with 4 heads attain comparable or better performance to the baseline transformers with 8 heads. © 2021, CC BY.

关键词： Benchmarking

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Retrospective for the dynamic sensorium competition for predicting large-scale mouse primary visual cortex activity from videos 24

Retrospective for the dynamic sensorium competition for pred...

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

作者： Polina Turishcheva Paul G. Fahey Michaela Vystrčilová Laura Hansel Rachel Froebe Kayla Ponder Yongrong Qiu Konstantin F. Willeke Mohammad Bashiri Ruslan Baikulov Yu Zhu Lei Ma Shan Yu Tiejun Huang Bryan M. Li Wolf De Wulf Nina Kudryashova Matthias H. Hennig Nathalie L. Rochefort Arno Onken Eric Wang Zhiwei Ding Andreas S. Tolias Fabian H. Sinz Alexander S. Ecker Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience & Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston Texas and Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine Stanford CA and Stanford Bio-X Stanford University Stanford CA and Wu Tsai Neurosciences Institute Stanford University Stanford CA Department of Neuroscience & Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston Texas Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany and Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine Stanford CA and Stanford Bio-X Stanford University Stanford CA and Wu Tsai Neurosciences Institute Stanford University Stanford CA Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany and International Max Planck Research School for Intelligent Systems Tübingen Germany and Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China and Beijing Academy of Artificial Intelligence China Beijing Academy of Artificial Intelligence China Institute of Automation Chinese Academy of Sciences China The Alan Turing Institute UK and School of Informatics University of Edinburgh UK School of Informatics University of Edinburgh UK Centre for Discovery Brain Sciences University of Edinburgh UK and Simons Initiative for the Developing Brain University of Edinburgh UK Department of Neuroscience & Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston Texas and Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine Stanford CA and Stanford Bio-X Stanford University Stanford CA and Wu Tsai Neurosciences Institute Stanford University Stanf

ISBN: (纸本)9798331314385

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different models on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 Competition Track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***.

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CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data

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Nature Immunology 2025年第6期26卷 963-974页

作者： Zhu, Bokai Gao, Sheng Chen, Shuxiao Wang, Yuchen Yeung, Jason Bai, Yunhao Huang, Amy Y. Yeo, Yao Yu Liao, Guanrui Mao, Shulin Jiang, Zhenghui G. Rodig, Scott J. Wong, Ka-Chun Shalek, Alex K. Nolan, Garry P. Jiang, Sizun Ma, Zongming Ragon Institute of MGH MIT and Harvard Cambridge MA United States Broad Institute of MIT and Harvard Cambridge MA United States Massachusetts Institute of Technology Cambridge MA United States Department of Statistics and Data Science The Wharton School University of Pennsylvania Philadelphia PA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States Department of Computer Science City University of Hong Kong Hong Kong Dana-Farber Cancer Institute Boston MA United States Center of Hepato-Pancreato-Biliary Surgery The First Affiliated Hospital of Sun Yat-sen University Guangzhou China Division of Genetics and Genomics Boston Children’s Hospital Harvard Medical School Boston MA United States Program in Biological and Biomedical Sciences Harvard Medical School Boston MA United States Division of Gastroenterology/Liver Center Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States Department of Pathology Brigham and Women’s Hospital Harvard Medical School Boston MA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA United States Department of Chemistry Massachusetts Institute of Technology Cambridge MA United States Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA United States Department of Pathology Stanford University Stanford CA United States Department of Statistics and Data Science Yale University New Haven CT United States

Delineating cell populations is crucial for understanding immune function in health and disease. Spatial omics technologies offer insights by capturing three complementary domains: single-cell molecular biomarker expression, cellular spatial relationships and tissue architecture. However, current computational methods often fail to fully integrate these multidimensional data, particularly for immune cell populations and intrinsic functional states. We introduce Cell Local Environment and Neighborhood Scan (CellLENS), a self-supervised computational method that learns cellular representations by fusing information across three spatial omics domains (expression, neighborhood and image). CellLENS markedly enhances de novo discovery of biologically relevant immune cell populations at fine granularity by integrating individual cells’ molecular profiles with their neighborhood context and tissue localization. By applying CellLENS to diverse spatial proteomic and transcriptomic datasets across multiple tissue types and disease settings, we uncover unique immune cell populations functionally stratified according to their spatial contexts. Our work demonstrates the power of multi-domain data integration in spatial omics to reveal insights into immune cell heterogeneity and tissue-specific functions. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks

arXiv

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arXiv 2023年

作者： Zhang, Kai Zhou, Rong Adhikarla, Eashan Yan, Zhiling Liu, Yixin Yu, Jun Liu, Zhengliang Chen, Xun Davison, Brian D. Ren, Hui Huang, Jing Chen, Chen Zhou, Yuyin Fu, Sunyang Liu, Wei Liu, Tianming Li, Xiang Chen, Yong He, Lifang Zou, James Li, Quanzheng Liu, Hongfang Sun, Lichao Department of Computer Science and Engineering Lehigh University PA United States School of Computing University of Georgia GA United States Samsung Research America CA United States Department of Radiology Massachusetts General Hospital Harvard Medical School MA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PA United States PolicyLab Children’s Hospital of Philadelphia PA United States Center for Research in Computer Vision University of Central Florida FL United States Department of Computer Science and Engineering University of California Santa CruzCA United States McWilliams School of Biomedical Informatics UTHealth HoustonTX United States Department of Radiation Oncology Mayo Clinic AZ United States University of Pennsylvania PA United States PA United States Leonard Davis Institute of Health Economics PA United States Department of Biomedical Data Science Stanford University School of Medicine CA United States Department of Computer Science Stanford University CA United States

Traditional biomedical artificial intelligence (ai) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist ai holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs. However, existing biomedical generalist ai solutions are typically heavyweight and closed source to researchers, practitioners, and patients. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model, designed as a generalist capable of performing various biomedical tasks. BiomedGPT achieved state-of-the-art results in 16 out of 25 experiments while maintaining a computing-friendly model scale. We also conducted human evaluations to assess the capabilities of BiomedGPT in radiology visual question answering, report generation, and summarization. BiomedGPT exhibits robust prediction ability with a low error rate of 3.8% in question answering, satisfactory performance with an error rate of 8.3% in writing complex radiology reports, and competitive summarization ability with a nearly equivalent preference score to human experts. Our method demonstrates that effective training with diverse data can lead to more practical biomedical ai for improving diagnosis and workflow efficiency. © 2023, CC BY.

关键词： Visual languages

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A Text Multi-label Classification Scheme Based on Resampling and Ensemble Learning 8th

A Text Multi-label Classification Scheme Based on Resampling...

引用

8th International Conference on Artificial Intelligence and Security , ICaiS 2022

作者： Wang, Tianhao Weng, Tianrang Ji, Jiacheng Zhong, Mingjun Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration in Southeast University Ministry of Education Nanjing211189 China Research Center for Judicial Big Data Supreme Count of China Nanjing211189 China Department of Computing Science University of Aberdeen AberdeenAB24 3UE United Kingdom

ISBN: (纸本)9783031067600

The medical dispute cases are professional and closely related to medicine. Therefore, the mediation of cases in practice depends heavily on similar historical cases. Multi-label classification of legal documents can efficiently filter irrelevant historical cases, which helps to recommend the similar historical cases faster and better. However, the imbalance and label symbiosis of the data set directly affect multi-label classification of legal documents. Therefore, a multi-label classification scheme based on resampling and ensemble learning is presented in this paper The scheme includes two parts: in the first part, in order to reduce the impact of label symbiosis on resampling, a resampling algorithm based on the average sparsity of the label set is proposed improve the imbalance of the data set;in the second one, a multi-label classification algorithm based on ensemble learning is proposed to train multiple base classifiers and combine each base classifier with a voting strategy of one vote. It can effectively improve the effect of multi-label classification. The experimental results show that the scheme proposed in this paper can improve the effect of multi-label classification and is not only suitable for legal documents but also applicable for other text data sets with imbalanced classes and label symbiosis problems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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Cavity approach for the approximation of spectral density of graphs with heterogeneous structures

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Physical Review E 2024年第3期109卷 034303-034303页

作者： Grover E. C. Guzman Peter F. Stadler Andre Fujita Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 São Paulo - SP 05508-090 Brazil Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics School of Excellence in Embedded Composite AI Dresden/Leipzig (SECAI) Leipzig University Härtelstraße 16-18 D-04107 Leipzig Germany German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Puschstraße 4 04103 Leipzig Germany Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Humboldtstraße 25 04105 Leipzig Germany Leipzig University Härtelstraße 16-18 D-04107 Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 D-04103 Leipzig Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 A-1090 Wien Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá 111321 Colombia and The Santa Fe Institute 1399 Hyde Park Road Santa Fe New Mexico 87501 USA Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 São Paulo - SP 05508-090 Brazil and Division of Network AI Statistics Medical Institute of Bioregulation Kyushu University Fukuoka 812-8582 Japan

Graphs have become widely used to represent and study social, biological, and technological systems. Statistical methods to analyze empirical graphs were proposed based on the graph's spectral density. However, their running time is cubic in the number of vertices, precluding direct application to large instances. Thus, efficient algorithms to calculate the spectral density become necessary. For sparse graphs, the cavity method can efficiently approximate the spectral density of locally treelike undirected and directed graphs. However, it does not apply to most empirical graphs because they have heterogeneous structures. Thus, we propose methods for undirected and directed graphs with heterogeneous structures using a new vertex's neighborhood definition and the cavity approach. Our methods' time and space complexities are O(|E|hmax3t) and O(|E|hmax2t), respectively, where |E| is the number of edges, hmax is the size of the largest local neighborhood of a vertex, and t is the number of iterations required for convergence. We demonstrate the practical efficacy by estimating the spectral density of simulated and real-world undirected and directed graphs.

关键词： Network structure Directed networks Monte Carlo methods Network Models Topological tools

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The Utility of Voided Urine Samples as a Proxy for the Vaginal Microbiome and for the Prediction of Bacterial Vaginosis

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Infectious Microbes & Diseases 2022年第4期4卷 149-156页

作者： Bin Zhu Christopher Diachok Laahirie Edupuganti David J.Edwards Jeffrey R.Donowitz Katherine Tossas Andrey Matveyev Katherine M.Spaine Vladimir Lee Myrna G.Serrano Gregory A.Buck Department of Microbiology and Immunology School of MedicineVirginia Commonwealth UniversityRichmondVirginiaUSA Center for Microbiome Engineering and Data Analysis Virginia Commonwealth UniversityRichmondVirginiaUSA Department of Statistical Sciences and Operations Research College of Humanities and SciencesVirginia Commonwealth UniversityRichmondVirginiaUSA Department of Pediatric Infectious Diseases Children's Hospital of Richmond at Virginia Commonwealth UniversityRichmondVirginiaUSA Department of Infectious Diseases and International Health University of VirginiaCharlottesvilleVirginiaUSA Department of Health Behavior and Policy School of MedicineVirginia Commonwealth UniversityRichmondVirginiaUSA Department of Family Medicine and Population Health School of MedicineVirginia Commonwealth UniversityRichmondVirginiaUSA Computer Science Department College of EngineeringVirginia Commonwealth UniversityRichmondVirginiaUSA Genomics Core Virginia Commonwealth UniversityRichmondVirginiaUSA

Recent work has shown that the vaginal microbiome exerts a strong impact on women's gynecological ***,collection of vaginal specimens is invasive and requires previous clinical training or the involvement of a trained *** contrast,urine sample collection is routine and noninvasive and does not require involvement of a *** sought to compare the vaginal and urogenital microbiomes to assess the utility of voided urine samples as a proxy for the vaginal *** urogenital and vaginal samples were collected from pregnant women and characterized by 16S rRNA taxonomic *** examined diversities and compositions of paired urogenital and vaginal microbiomes using five discrete strategies to explore the similarity between the vaginal and urogenital microbiomes.A strategy comparing the paired urogenital and vaginal microbiomes in which taxa were assigned using the STIRRUPS database and urine-specific taxa were removed showed no significant difference in diversity and composition between the paired urogenital and vaginal ***,the relative abundances of common vaginal taxa were linearly correlated with those in the paired *** similarities suggest that voided urine samples could represent a noninvasive protocol for accurate profiling of the vaginalmicrobiome with likely clinical ***,a machine learning model was established in which the voided urine microbiome was compared favorably to the vaginal microbiome in predicting bacterial vaginosis.

关键词： 16S rRNA vaginal microbiome urine microbiome bacterial vaginosis reproductive and gynecologic health

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Advances in artificial intelligence techniques drive the application of radiomics in the clinical research of hepatocellular carcinoma

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iLIVER 2022年第1期1卷 49-54页

作者： Jingwei Wei Meng Niu Ouyang Yabo Yu Zhou Xiaoke Ma Xue Yang Hanyu Jiang Hui Hui Hongyi Cao Binwei Duan Hongjun Li Dawei Ding Jie Tian Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Molecular Imaging Beijing 100190China Department of Interventional Radiology The First Affiliated Hospital of China Medical UniversityShenyangLiaoning110000China Beijing YouAn Hospital Capital Medical UniversityBeijing Institute of HepatologyBeijing100069China School of Life Science and Technology Xidian UniversityXi'anChina School of Computer Science and Technology Xidian UniversityXi'anShaanxiChina Department of Radiology Beijing Youan HospitalCapital Medical UniverstiyBeijing100069China Department of Radiology West China HospitalSichuan UniversityChengduSichuan 610041China Department of Pathology College of Basic Medical ScienceChina Medical UniversityShenyangLiaoning110000China The Department of General Surgery Center Beijing YouAn HospitalCapital Medical UniversityChina School of Bioengineering Beihang UniversityBeijing100191China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine Beihang UniversityBeijing100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi'anShaanxi710126China

Hepatocellular carcinoma(HCC)remains the most common malignancy to threaten public health *** advances in artificial intelligence techniques,radiomics for HCC management provides a novel perspective to solve unmet needs in clinical settings,and reveals pixel-level radiological information for medical imaging big data,correlating the radiological phenotype with targeted clinical *** radiomics pipelines depend on handcrafted engineering features,and further deep learning-based radiomics pipelines are supplemented with deep features calculated via self-learning *** the past decade,radiomics has been widely applied in accurate diagnoses and pathological or biological behavior evaluation,as well as in prognosis *** this review,we systematically introduce the main pipelines of artificial intelligence-based radiomics and their efficacy in the clinical studies of HCC.

关键词： Artificial intelligence Diagnosis Hepatocellular carcinoma Prognosis Radiomics

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