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A Real-Time Interpretable Ai Model for the Cholangioscopic Diagnosis of Malignant Biliary Stricture

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SSRN

SSRN 2022年

作者： Zhang, Xiang Tang, Dehua Zhou, Jindong Ni, Muhan Yan, Peng Zhang, Zhenyu Yu, Tao Zhan, Qiang Shen, Yonghua Zhou, Lin Zheng, Ruhua Zou, Xiaoping Zhang, Bin Li, Wu-Jun Wang, Lei Department of Gastroenterology Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University Nanjing Medical University Jiangsu Nanjing210008 China Department of Gastroenterology Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University Jiangsu Nanjing210008 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210008 China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Jiangsu Nanjing210008 China Departments of Gastroenterology Qilu Hospital of Shandong University Shandong Jinan China Department of Gastroenterology Wuxi People’s Hospital Affiliated to Nanjing Medical University Jiangsu Wuxi214023 China Department of Gastroenterology Taikang Xianlin Drum Tower Hospital Jiangsu Nanjing210046 China Center for Medical Big Data Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University Jiangsu Nanjing210008 China Department of Gastroenterology Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University 321 Zhongshan Road Jiangsu Nanjing210008 China

Background: It is crucial to accurately determine malignant biliary strictures (MBSs) for early curative treatment. Therefore, an effective and accessible modality is needed to facilitate the diagnosis of MBSs. The study aimed to develop a real-time interpretable artificial intelligent (AI) system to predict MBSs under digital single-operator cholangioscopy (DSOC).Methods: A novel interpretable AI system called MBSDeiT was developed, consisting of two models to identify qualified images and then predict MBS in real time. The overall efficiency of MBSDeiT was validated at the image level on internal, external, prospective testing datasets and subgroups analyses, and at the video level on the prospective test datasets, and compared with that of endoscopists. The association between AI predictions and endoscopic features was evaluated to increase the ***: MBSDeiT can first automatically select qualified DSOC images with an AUC of 0.904 and 0.921–0.927 on the internal testing dataset and the external testing datasets, and then identify MBSs with an AUC of 0.971 on the internal testing dataset, an AUC of 0.978–0.999 on the external testing datasets, and an AUC of 0.976 on the prospective testing dataset, respectively. MBSDeiT accurately identified 92.3% MBS in prospective testing videos. Subgroups analyses confirmed the stability and robustness of MBSDeiT. MBSDeiT achieved superior performance to that of expert and novice endoscopists. The AI predictions were significantly associated with four endoscopic features [including mass (OR 25.17, 95% CI 8.73–72.61);friability (70.70, 16.89–296.03);raised intraductal lesion (10.94, 5.68–21.04);and abnormal vessels (103.08, 13.94–762.09)] under DSOC, which is consistent with the endoscopists’ ***: The findings suggest that MBSDeiT could be a promising approach for the accurate diagnosis of MBS under *** Information: The project is funded by Jiangsu Provincial Health Commission

关键词： Artificial intelligence

Generative Adversarial Networks for Image Super-Resolution: A Survey

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arXiv 2022年

作者： Tian, Chunwei Zhang, Xuanyu Zhu, Qi Zhang, Bob Lin, Jerry Chun-Wei School of Software Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-space-Ground-Ocean Big Data Application Technology 1 Dongxiang Rd Shaanxi Xi’an710129 China School of Software Northwestern Polytechnical University 1 Dongxiang Rd Shaanxi Xi’an710129 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics 101 Section 2 Kuang-Fu Road Jiangsu Nanjing China PAMI Research Group University of Macau 101 Section 2 Kuang-Fu Road Macao999078 China Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

Topological Machine Learning for Multivariate Time Series

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arXiv 2019年

作者： Wu, Chengyuan Hargreaves, Carol Anne Data Analytics Consulting Centre Department of Statistics and Applied Probability Faculty of Science National University of Singapore Singapore

We develop a framework for analyzing multivariate time series using topological data analysis (TDA) methods. The proposed methodology involves converting the multivariate time series to point cloud data, calculating Wasserstein distances between the persistence diagrams and using the k-nearest neighbors algorithm (k-NN) for supervised machine learning. Two methods (symmetry-breaking and anchor points) are also introduced to enable TDA to better analyze data with heterogeneous features that are sensitive to translation, rotation, or choice of coordinates. We apply our methods to room occupancy detection based on 5 time-dependent variables (temperature, humidity, light, CO2 and humidity ratio). Experimental results show that topological methods are effective in predicting room occupancy during a time window. Copyright © 2019, The Authors. All rights reserved.

关键词： k-nearest neighbors

Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

EpiBeds: data informed modelling of the COVID-19 hospital burden in England

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arXiv 2021年

作者： Overton, Christopher E. Pellis, Lorenzo Stage, Helena B. Scarabel, Francesca Burton, Joshua Fraser, Christophe Hall, Ian House, Thomas A. Jewell, Chris Nurtay, Anel Pagani, Filippo Lythgoe, Katrina A. Department of Mathematics University of Manchester United Kingdom Clinical Data Science Unit Manchester University NHS Foundation Trust United Kingdom Joint UNIversities Pandemic and Epidemiological Research Humboldt University of Berlin Germany University of Potsdam Germany MRC Biostatistics Unit University of Cambridge United Kingdom Division of Informatics Imaging and Data Sciences Faculty of Biology Medicine and Health University of Manchester United Kingdom CHICAS Lancaster Medical School Lancaster University United Kingdom Big Data Institute Nuffield Department of Medicine University of Oxford United Kingdom Department of Zoology University of Oxford United Kingdom Wellcome Centre for Human Genetics Nuffield Department of Medicine NIHR Biomedical Research Centre University of Oxford United Kingdom Wellcome Sanger Institute Cambridge United Kingdom Alan Turing Institute United Kingdom Emergency Preparedness Health Protection Division Public Health England United Kingdom

The first year of the COVID-19 pandemic put considerable strain on the national healthcare system in England. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, which was coupled to a model of the generalised epidemic. We named this model EpiBeds. data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow different clinical pathways, and the reproduction number of the generalised epidemic. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds has provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland. © 2021, CC BY.

关键词： Hospitals

Machine learning applications for therapeutic tasks with genomics data

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arXiv 2021年

作者： Huang, Kexin Xiao, Cao Glass, Lucas M. Critchlow, Cathy W. Gibson, Greg Sun, Jimeng Health Data Science Program Harvard University Boston02120 United States Analytics Center of Excellence IQVIA CambridgeMA02139 United States Center for Observational Research Amgen Thousand OaksCA91320 United States Center for Integrative Genomics Georgia Institute of Technology AtlantaGA30332 United States Department of Computer Science Carle's Illinois College of Medicine University of Illinois at Urbana-Champaign Urbana61820 United States

The genome contains instructions for building the function and structure, and guiding the evolution of molecules and organisms. Recent high-throughput techniques allow the generation of a vast amount of genomics data. However, the path of transforming genomics data into tangible therapeutics is filled with obstacles. We observe that genomics data alone are insufficient but rather require investigation of its interplay with data such as compounds, proteins, electronic health records, images, texts, etc. To make sense of these complex data, machine learning techniques are often utilized for identifying patterns and drawing insights from data. In this review, we study twenty-two genomics applications of machine learning that can enable faster and more efficacious therapeutic development, from discovering novel targets, personalized medicine, developing gene-editing tools all the way to clinical trials and post-market studies. Challenges remain, including technical issues such as learning under different contexts given specific low resource constraints, and practical issues such as mistrust of models, privacy, and fairness. Copyright © 2021, The Authors. All rights reserved.

关键词： Genes

From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology

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arXiv 2023年

作者： El Nahhas, Omar S.M. van Treeck, Marko Wölflein, Georg Unger, Michaela Ligero, Marta Lenz, Tim Wagner, Sophia J. Hewitt, Katherine J. Khader, Firas Foersch, Sebastian Truhn, Daniel Kather, Jakob Nikolas Else Kroener Fresenius Center for Digital Health Medical Faculty Carl Gustav Carus Technical University Dresden Dresden Germany School of Computer Science University of St Andrews St Andrews United Kingdom Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany Institute of Pathology University Medical Center Mainz Mainz Germany Pathology & Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds United Kingdom University Hospital Heidelberg Heidelberg Germany Department of Medicine 1 University Hospital Faculty of Medicine Carl Gustav Carus Technische Universität Dresden Dresden Germany

Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic- and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages which have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of MSI-high tumors. Moreover, we provide an open-source codebase which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge. © 2023, CC BY.

关键词： Pathology

Fabrication And Analysis Of 3d Wind Sonic Using A Dual Transduser Sensor Arrangement

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Journal of Physics: Conference Series 2021年第1期1951卷

作者： E E Barus M S Muntini A C Louk Department of Physics Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia Department of Physics Faculty of Science and Engineering University of Nusa Cendana Kupang 85001 Indonesia

In this research, Wind Sonic 3D fabrication and analysis have been carried out using the time of flight method and dual transducer sensor arrangement. The distance between the transducers (transmitter 1 – receiver 1) on each axis is 16 cm and the distance between the transducers (transmitter 1-receiver 2 and transmitter 2-receiver 1) is 3 mm and 10 mm. Wind sonic 3D uses a 40 kHz ultrasonic sensor. The wind sonic sensor calibration process is carried out in two conditions, namely conditions without interference and conditions with a wind speed of 2.3 m/s at two variations of the distance between the transducers. At the distance between the 3 mm transducer and the state without interference, the measurement range obtained is - 0.19 – 0.19 m/s and the state of wind speed is 2.3 m/s. The difference in the measurement range is -0.19 – 0.29 m/s with a percentage error value between 0-12.62 %. At the distance between the transducer of 10 mm with a state without interference, the measurement range obtained is 0 – 0.14 m/s and the state of wind speed is 2.3 m/s the difference in the measurement range is 0 – 0.21 m/s with the percentage error value on the X-axis between 1.28% - 9.14%, Y-axis between 0.29% - 8.61% and Z-axis between 0.05% - 7.44%. Based on the results of the calibration, it can be seen that to use a dual transducer sensor arrangement in wind sonic 3D, the minimum distance between the transducers is 10 mm to reduce the occurrence of wave interference and measurement errors.

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PAI-1-driven SFRP2^high cancer-associated fibroblasts hijack the abscopal effect of radioimmunotherapy

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Cancer Cell 2025年第5期43卷 856-874.e9页

作者： Zhang, Yan-Pei Guo, Ze-Qin Cai, Xiao-Ting Rong, Zi-Xuan Fang, Yuan Chen, Jia-Qi Zhuang, Kui-Mao Ruan, Min-Jie Ma, Si-Cong Lin, Le-Yi Han, Duan-Duan Li, Yang-Si Wang, Yuan-Yuan Wang, Jian Cao, Chuan-Hui Tang, Xin-Ran Xie, Qian-Kun Chen, Yue Lin, Yan Tan, Jia-Le Yu, Zi-Hang Wu, Ze-Nan Wei, Wei Zheng, Da-Yong Zeng, Yu-Jie Ruan, Ying-Chen Xu, Zi-Peng Gu, Jun-Zi Xiao, Lu-Shan Liu, Li Guan, Jian Bai, Xue Wu, De-Hua Dong, Zhong-Yi Department of Radiation Oncology Nanfang Hospital Southern Medical University Guangzhou 510515 China Guangdong Lung Cancer Institute Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangdong Guangzhou 510515 China Department of Oncology Nanfang Hospital Southern Medical University Guangzhou 510515 China Department of Oncology Xiangyang Central Hospital Hubei University of Arts and Science Hubei Xiangyang 441000 China Department of Oncology Shunde Hospital Southern Medical University (The First People's Hospital of Shunde Foshan) Shunde 528333 China Institute of Cancer Stem Cell Dalian Medical University Dalian 116044 China Big Data Center Nanfang Hospital Southern Medical University Guangzhou 510515 China Guangdong Provincial Key Laboratory of Viral Hepatitis Research Hepatology Unit and Department of Infectious Diseases Nanfang Hospital Southern Medical University Guangzhou 510515 China Department of Health Management Center Nanfang Hospital Southern Medical University Guangzhou 510515 China

The abscopal effect of radioimmunotherapy, wherein tumor shrinkage occurs beyond the irradiated field, is therapeutically promising but clinically rare. The mechanisms underlying this effect remain elusive. Here, in vivo genome-wide CRISPR screening identifies SFRP2 as a potential stromal regulator of the abscopal effect. SFRP2 exhibits cancer-associated fibroblast (CAF)-specific expression and radioimmunotherapy-mediated upregulation in unirradiated tumors. Conditional Sfrp2 knockout in CAFs boosts the abscopal effect by rewiring the vascular-immune microenvironment to promote CD8⁺ T cell recruitment to unirradiated tumors. In vivo lineage tracing reveals that elevated SFRP2 correlates with radioimmunotherapy-driven pericyte lineage commitment. Serum proteomics reveals that irradiated-tumor-secreted PAI-1 triggers distant tumor pericyte cell-fate transition into SFRP2^high CAFs via the LRP1/p65 axis. Pharmacologically blocking SFRP2 or PAI-1 enhances the abscopal effect in humanized patient-derived xenograft models. Our findings collectively illustrate that PAI-1-induced SFRP2^high CAFs serve as critical stromal regulator to hijack the abscopal effect, providing promising targets for enhancing radioimmunotherapy effectiveness. © 2025 Elsevier Inc.

关键词： abscopal effect cancer-associated fibroblasts PAI-1 pericytes radioimmunotherapy SFRP2 stromal lineage