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Medicine Advances 2023年第3期1卷

作者： Meng Huang Gu Gong Yicheng Deng Xinmiao Long Wenyong Long Qing Liu Wei Zhao Rufu Chen Guangdong Cardiovascular Institute Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Medical Research Institute Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou China Contribution: Formal analysis (lead) Investigation (lead) Resources (lead) Visualization (lead) Writing - original draft (lead) Department of Neurosurgery in Xiangya Hospital Central South University Changsha China Contribution: Data curation (supporting) Validation (supporting) Writing - original draft (supporting) Zhongshan School of Medicine Sun Yat-sen University Guangzhou China Contribution: Formal analysis (equal) Investigation (supporting) Visualization (equal) Department of Pathogeny Biology School of Basic Medical Science Central South University Changsha Hunan China Contribution: Data curation (supporting) Investigation (supporting) Software (supporting) Contribution: Investigation (supporting) Visualization (supporting) Contribution: Methodology (supporting) Writing - review & editing (supporting) Rufu Chen and Wei Zhao Guangdong Provincial People's Hospital No.106 ZhongshanRoad II Guangzhou 510080 China. Contribution: Conceptualization (equal) Project administration (equal) Supervision (equal) Writing - review & editing (equal)

Crosstalk between tumors and the nervous system has emerged as a significant hallmark of human cancer. In the central nervous system, neurons closely interact with tumor cells, promoting the proliferation of glioma and neuroblastoma. Additionally, the peripheral nervous system plays a crucial role in reshaping the tumor microenvironment, modulating angiogenesis, and regulating immune cell function, while also directly promoting tumorigenesis and metastasis. Current research has elucidated some of the specific neural signaling mechanisms involved in this crosstalk, including neurotransmitters, neuropeptides, and growth factors. In this review, we aim to summarize these mechanisms and highlight the latest discoveries in various solid tumors, such as glioma, pancreatic, prostate, and gastrointestinal cancers. By understanding the intricate crosstalk between cancer cells and the nervous system, we can develop more effective and targeted treatments for cancer patients.

关键词： cell crosstalk central nervous system in cancer neurotransmitters peripheral nervous system in cancer tumor microenvironment

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Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII

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database 2023年第2023期2023卷 baad005-baad005页

作者： Leaman, Robert Islamaj, Rezarta Adams, Virginia Alliheedi, Mohammed A. Almeida, João Rafael Antunes, Rui Bevan, Robert Chang, Yung-Chun Erdengasileng, Arslan Hodgskiss, Matthew Ida, Ryuki Kim, Hyunjae Li, Keqiao Mercer, Robert E. Mertová, Lukrécia Mobasher, Ghadeer Shin, Hoo-Chang Sung, Mujeen Tsujimura, Tomoki Yeh, Wen-Chao Lu, Zhiyong National Center for Biotechnology Information (NCBI) National Library of Medicine National Institutes of Health 8600 Rockville Pike Bethesda 20894 MD United States Nvidia 2788 San Tomas Expressway Santa Clara 95051 CA United States Department of Computer Science Al Baha University 4781 King Fahd Rd Al Aqiq 65779 Saudi Arabia Department of Electronics Telecommunications and Informatics (DETI) Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Department of Information and Communications Technologies University of A Coruña Camiño Do Lagar de Castro A Coruña 15008 Spain Informatics Department Medicines Discovery Catapult Alderley Park Block 35 Mereside Macclesfield SK10 4ZF United Kingdom Graduate Institute of Data Science Taipei Medical University No. 172-1 Section 2 Keelung Rd Da’an District Taipei City 106 Taiwan Department of Statistics Florida State University 117 N. Woodward Ave Tallahassee 32306 FL United States Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata Tempaku-ku Nagoya Aichi 468-8511 Japan Department of Computer Science and Engineering Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 South Korea Department of Computer Science The University of Western Ontario Middlesex College Room 355 London N6A 5B7 ON Canada Scientific Databases and Visualization Group Heidelberg Institute for Theoretical Studies (HITS GGmbH) Schloss-Wolfsbrunnenweg 35 Heidelberg 69118 Germany Institute of Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg 69120 Germany Institute of Information Systems and Applications National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu 30013 Taiwan

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and - as highlighted during the coronavirus disease 2019 pandemic - their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substanti

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π–π Interaction-Induced Organic Long-wavelength Room-Temperature Phosphorescence forIn VivoAtherosclerotic Plaque Imaging

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Angewandte Chemie 2023年第2期136卷

作者： Yufan Zhang Jisen Li Jiliang Zhao Xuefei Li Zhimei Wang Yicheng Huang Prof. Hongkai Zhang Prof. Qian Liu Prof. Yunxiang Lei Prof. Dan Ding Frontiers Science Center for Cell Responses State Key Laboratory of Medicinal Chemical Biology Key Laboratory of Bioactive Materials Ministry of Education College of Life Sciences Nankai University 300071 Tianjin China Contribution: Conceptualization (equal) Data curation (equal) Formal analysis (lead) Writing - original draft (lead) Contribution: Conceptualization (equal) Data curation (equal) Formal analysis (equal) Contribution: Investigation (equal) Methodology (equal) Contribution: Conceptualization (supporting) Software (equal) School of Chemistry and Materials Engineering Wenzhou University 325035 Wenzhou China Contribution: Investigation (supporting) Methodology (supporting) Contribution: Conceptualization (supporting) Data curation (supporting) Department of Urology Tianjin First Central Hospital 300192 Tianjin China Contribution: Data curation (supporting) Software (supporting) Contribution: Visualization (lead) Writing - review & editing (supporting) Contribution: Funding acquisition (lead) Writing - review & editing (lead)

Room-temperature phosphorescent (RTP) materials have great potential for in vivo imaging because they can circumvent the autofluorescence of biological tissues. In this study, a class of organic-doped long-wavelength (≈600 nm) RTP materials with benzo[c][1,2,5] thiadiazole as a guest was constructed. Both host and guest molecules have simple structures and can be directly purchased commercially at a low cost. Owing to the long phosphorescence wavelength of the doping system, it exhibited good tissue penetration (10 mm). Notably, these RTP nanoparticles were successfully used to image atherosclerotic plaques, with a signal-to-background ratio (SBR) of 44.52. This study provides a new approach for constructing inexpensive red organic phosphorescent materials and a new method for imaging cardiovascular diseases using these materials.

关键词： Atherosclerotic Plaque Imaging Benzo[c][1,2,5] Thiadiazole Guest Host-Guest Doped Systems Room Temperature Phosphorescence

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Universal Sub-Nanoreactor Strategy for Synthesis of Yolk-Shell MoS2Supported Single Atom Electrocatalysts toward Robust Hydrogen Evolution Reaction

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Angewandte Chemie 2023年第40期135卷

作者： Feilong Gong Yuheng Liu Yang Zhao Prof. Wei Liu Prof. Guang Zeng Prof. Guoqing Wang Prof. Yonghui Zhang Lihua Gong Prof. Jian Liu Key Laboratory of Surface and Interface Science and Technology of Henan Province College of Material and Chemical Engineering Zhengzhou University of Light Industry Zhengzhou Henan 450001 P. R. China Contribution: Conceptualization (equal) Formal analysis (equal) Funding acquisition (lead) Investigation (lead) Visualization (equal) Writing - original draft (lead) Contribution: Data curation (lead) Formal analysis (equal) Investigation (equal) Validation (equal) Visualization (equal) State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian Liaoning 116023 P. R. China Contribution: Data curation (equal) Formal analysis (equal) Resources (equal) Validation (equal) Visualization (equal) Contribution: Data curation (equal) Formal analysis (equal) Investigation (equal) Contribution: Data curation (equal) Methodology (equal) Software (equal) Validation (equal) Contribution: Formal analysis (equal) Software (equal) Writing - review & editing (equal) Contribution: Conceptualization (equal) Data curation (equal) Formal analysis (lead) Software (lead) Writing - review & editing (equal) DICP-Surrey Joint Centre for Future Materials Department of Chemical and Process Engineering and Advanced Technology Institute of University of Surrey Guildford Surrey GU2 7XH UK College of Chemistry and Chemical Engineering Inner Mongolia University Hohhot Inner Mongolia 010021 P. R. China Contribution: Conceptualization (lead) Formal analysis (equal) Funding acquisition (equal) Project administration (lead) Supervision (lead) Writing - review & editing (lead)

The coordination structure determines the electrocatalytic performances of single atom catalysts (SACs), while it remains a challenge to precisely regulate their spatial location and coordination environment. Herein, we report a universal sub-nanoreactor strategy for synthesis of yolk-shell MoS 2 supported single atom electrocatalysts with dual-anchored microenvironment of vacancy-enriched MoS 2 and intercalation carbon toward robust hydrogen-evolution reaction. Theoretical calculations reveal that the “E-Lock” and “E-Channel” are conducive to stabilize and activate metal single atoms. A group of SACs is subsequently produced with the assistance of sulfur vacancy and intercalation carbon in the yolk-shell sub-nanoreactor. The optimized C−Co−MoS 2 yields the lowest overpotential (η 10 =17 mV) compared with previously reported MoS 2 -based electrocatalysts to date, and also affords a 5–9 fold improvement in activity even comparing with those as-prepared single-anchored analogues. Theoretical results and in situ characterizations unveil its active center and durability. This work provides a universal pathway to design efficient catalysts for electro-refinery.

关键词： Electrocatalyst Nanostructures Sub-Nanoreactor Universal Strategy Water Splitting

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Dynamic class learning approach for smart CBIR 6th

Dynamic class learning approach for smart CBIR

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6th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2017

作者： Pahariya, Girraj Ravindran, Balaraman Das, Sukhendu Department of Computer Science and Engineering IIT Madras Chennai India Robert Bosch Centre for Data Science and AI IIT Madras Chennai India Visualization and Perception Lab IIT Madras Chennai India

ISBN: (纸本)9789811300196

Smart Content Based Image Retrieval (CBIR) helps to simultaneously localize and recognize all object(s) present in a scene, for image retrieval task. The major drawbacks in such kind of system are: (a) overhead for addition of new class is high - addition of new class requires manual annotation of large number of samples and retraining of an entire object model;and (b) use of handcrafted features for recognition and localization task, which limits its performance. In this era of data proliferation where it is easy to discover new object categories and hard to label all of them i.e. less amount of labeled samples for training which raises the above mentioned drawbacks. In this work, we propose an approach which cuts down the overhead of labelling the data and re-training on an entire module to learn new classes. The major components in proposed framework are: (a) selection of an appropriate pre-trained deep model for learning a new class;and (b) learning new class by utilizing selected deep model with less supervision (i.e. with the least amount of labeled data) using a concept of triplet learning. To show the effectiveness of the proposed technique of new class learning, we have performed an evaluation on CIFAR-10, PASCAL VOC2007 and Imagenet datasets. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Content based retrieval

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Deploying Cationic Cellulose Nanofiber Confinement to Enable High Iodine Loadings Towards High Energy and High-Temperature Zn-I2Battery

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Angewandte Chemie 2023年第5期136卷

作者： Zhenglin Li Wenwen Cao Tao Hu Yichan Hu Rong Zhang Huilin Cui Funian Mo Chaozheng Liu Chunyi Zhi Guojin Liang Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen 518055 China Co-Innovation Center of Efficient Processing and Utilization of Forest Resources College of Materials Science and Engineering Nanjing Forestry University Nanjing 210000 China Contribution: Data curation (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead) School of Chemistry and Chemical Engineering University of Jinan Jinan 250022 China Contribution: Conceptualization (equal) Data curation (lead) Investigation (lead) Methodology (lead) School of Materials Science and Engineering Key Laboratory of Structure and Functional Regulation of Hybrid Materials Ministry of Education Anhui University Hefei 230601 China Contribution: Data curation (equal) Formal analysis (equal) Investigation (supporting) Methodology (supporting) Contribution: Conceptualization (equal) Data curation (supporting) Formal analysis (equal) Investigation (equal) Department of Materials Science and Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong 999077 Hong Kong Contribution: Data curation (equal) Investigation (equal) Methodology (equal) Contribution: Conceptualization (supporting) Data curation (supporting) Formal analysis (equal) Investigation (equal) Shenzhen Key Laboratory of Flexible Printed Electronics Technology Center Harbin Institute of Technology Shenzhen Guangdong 518055 China Contribution: Conceptualization (lead) Data curation (lead) Resources (lead) Supervision (equal) Contribution: Conceptualization (equal) Data curation (lead) Investigation (lead) Supervision (equal) Contribution: Supervision (lead) Visualization (lead) Writing - review & editing (lead) Contribution: Conceptualization (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead)

High iodine loading and high-temperature adaptability of the iodine cathode are prerequisites to achieving high energy density at full battery level and promoting the practical application for the zinc-iodine (Zn-I 2 ) battery. However, it would aggravate the polyiodide shuttle effect when employing high iodine loading and working temperature. Here, a sustainable cationic cellulose nanofiber (cCNF) was employed to confine the active iodine species through strong physiochemical adsorption to enlarge the iodine loading and stabilize it even at high temperatures. The cCNF could accommodate dual-functionality by enlarging the iodine loading and suppressing the polyiodide shuttle effect, owing to the unique framework structure with abundant surface positive charges. As a result, the iodine cathode based on the cCNF could deliver high iodine mass loading of 14.1 mg cm −2 with a specific capacity of 182.7 mAh g −1 , high areal capacity of 2.6 mAh cm −2 , and stable cycling over 3000 cycles at 2 A g −1 , thus enabling a high energy density of 34.8 Wh kg −1 and the maximum power density of 521.2 W kg −1 at a full Zn-I 2 battery level. In addition, even at a high temperature of 60 °C, the Zn-I 2 battery could still deliver a stable cycling.

关键词： Cationic Cellulose High- Temperature Battery Iodine Shuttle-Free Zn-I2 Battery

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Probability-turbulence divergence: A tunable allotaxonometric instrument for comparing heavy-tailed categorical distributions

arXiv

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arXiv 2020年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05401 United States Vermont Advanced Computing Center University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States MassMutual Data Science AmherstMA01002 United States Howard Hughes Medical Institute University of California Berkeley BerkeleyCA94720 United States Data Visualization Lab Khoury College of Computer Sciences Northeastern University BostonMA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

Real-world complex systems often comprise many distinct types of elements as well as many more types of networked interactions between elements. When the relative abundances of types can be measured well, we further observe heavy-tailed categorical distributions for type frequencies. For the comparison of type frequency distributions of two systems or a system with itself at different time points in time—a facet of allotaxonometry—a great range of probability divergences are available. Here, we introduce and explore ‘probability-turbulence divergence’, a tunable, straightforward, and interpretable instrument for comparing normalizable categorical frequency distributions. We model probability-turbulence divergence (PTD) after rank-turbulence divergence (RTD). While probability-turbulence divergence is more limited in application than rank-turbulence divergence, it is more sensitive to changes in type frequency. We build allotaxonographs to display probability turbulence, incorporating a way to visually accommodate zero probabilities for ‘exclusive types’ which are types that appear in only one system. We explore comparisons of example distributions taken from literature, social media, and ecology. We show how probability-turbulence divergence either explicitly or functionally generalizes many existing kinds of distances and measures, including, as special cases, L(p) norms, the Sørensen-Dice coefficient (the F1 statistic), and the Hellinger distance. We discuss similarities with the generalized entropies of Rényi and Tsallis, and the diversity indices (or Hill numbers) from ecology. We close with thoughts on open problems concerning the optimization of the tuning of rank- and probability-turbulence divergence. Copyright © 2020, The Authors. All rights reserved.

关键词： Normal distribution

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Toward a Domain-Overarching Metadata Schema for Making Health Research Studies FAIR (Findable, Accessible, Interoperable, and Reusable): Development of the NFDI4Health Metadata Schema

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JMIR medical informatics 2025年 13卷 e63906页

作者： Haitham Abaza Aliaksandra Shutsko Sophie A I Klopfenstein Carina N Vorisek Carsten Oliver Schmidt Claudia Brünings-Kuppe Vera Clemens Johannes Darms Sabine Hanß Timm Intemann Franziska Jannasch Elisa Kasbohm Birte Lindstädt Matthias Löbe Katharina Nimptsch Ute Nöthlings Marisabel Gonzalez Ocanto Tracy Bonsu Osei Ines Perrar Manuela Peters Tobias Pischon Ulrich Sax Matthias B Schulze Florian Schwarz Carolina Schwedhelm Sylvia Thun Dagmar Waltemath Hannes Wünsche Atinkut A Zeleke Wolfgang Müller Martin Golebiewski Scientific Databases and Visualization Heidelberg Institute for Theoretical Studies (HITS) Heidelberg Germany. Information Centre for Life Sciences German National Library of Medicine (ZB MED) Cologne Germany. Core Facility Digital Medicine and Interoperability Universitätsmedizin Berlin Berlin Institute of Health at Charité Berlin Germany. Institute for Community Medicine University Medicine Greifswald Greifswald Germany. Department of Biometry and Data Management Leibniz Institute for Prevention Research and Epidemiology - BIPS Bremen Germany. Department of Medical Informatics University Medical Center Göttingen Georg-August-University Göttingen Germany. Department of Molecular Epidemiology German Institute of Human Nutrition Potsdam-Rehbruecke Nuthetal Germany. Institute for Medical Informatics Statistics and Epidemiology University Leipzig Leipzig Germany. Molecular Epidemiology Research Group Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany. Department of Nutrition and Food Science University of Bonn Bonn Germany. Information and Research Data Management Robert Koch Institute Berlin Germany.

BACKGROUND:Despite wide acceptance in medical research, implementation of the FAIR (findability, accessibility, interoperability, and reusability) principles in certain health domains and interoperability across data sources remain a challenge. While clinical trial registries collect metadata about clinical studies, numerous epidemiological and public health studies remain unregistered or lack detailed information about relevant study documents. Making valuable data from these studies available to the research community could improve our understanding of various diseases and their risk factors. The National Research data Infrastructure for Personal Health data (NFDI4Health) seeks to optimize data sharing among the clinical, epidemiological, and public health research communities while preserving privacy and ethical regulations. OBJECTIVE:We aimed to develop a tailored metadata schema (MDS) to support the standardized publication of health studies' metadata in NFDI4Health services and beyond. This study describes the development, structure, and implementation of this MDS designed to improve the FAIRness of metadata from clinical, epidemiological, and public health research while maintaining compatibility with metadata models of other resources to ease interoperability. METHODS:Based on the models of dataCite, ***, and other data models and international standards, the first MDS version was developed by the NFDI4Health Task Force COVID-19. It was later extended in a modular fashion, combining generic and NFDI4Health use case-specific metadata items relevant to domains of nutritional epidemiology, chronic diseases, and record linkage. Mappings to schemas of clinical trial registries and international and local initiatives were performed to enable interfacing with external resources. The MDS is represented in Microsoft Excel spreadsheets. A transformation into an improved and interactive machine-readable format was completed using the ART-DECOR (Advanced

关键词： FAIR accessibility chronic diseases clinical trial data models data sharing epidemiological epidemiology findability health research implementation interoperability development metadata standards nutrition public health reusability structure tool

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A Versatile Strategy for Screening Custom-Designed Warhead-Armed Cyclic Peptide Inhibitors

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Angewandte Chemie 2022年第7期135卷

作者： Deokhee Kang Do-Wook Kim Joo-Chan Kim Prof. Hee-Sung Park Department of Chemistry Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon 34141 Korea Contribution: Conceptualization (equal) Data curation (equal) Methodology (equal) Validation (equal) Writing - original draft (equal) Contribution: Conceptualization (equal) Data curation (equal) Methodology (equal) Validation (equal) Writing - original draft (supporting) Contribution: Formal analysis (supporting) Methodology (supporting) Visualization (supporting) Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Funding acquisition (lead) Investigation (lead) Project administration (lead) Resources (lead) Supervision (lead) Validation (lead) Writing - original draft (lead) Writing - review & editing (lead)

Demand for peptide-based pharmaceuticals has been steadily increasing, but only limited success has been achieved to date. To expedite peptide-based drug discovery, we developed a general scheme for cell-based screening of cyclic peptide inhibitors armed with a user-designed warhead. We combined unnatural amino acid incorporation and split intein-mediated peptide cyclization techniques and integrated a yeast-based colorimetric screening assay to generate a new scheme that we call the custom-designed warhead-armed cyclic peptide screening platform (CWCPS). This strategy successfully discovered a potent inhibitor, CY5-6Q, that targets human histone deacetylase 8 (HDAC8) with a K D value of 15 nM. This approach can be a versatile and general platform for discovering cyclic peptide inhibitors.

关键词： Cyclic Peptide Inhibitor Genetic Code Expansion Warhead-Bearing Unnatural Amino Acid

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Attention-guided sampling for colorectal cancer analysis with digital pathology

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Journal of Pathology Informatics 2022年 13卷 100110-100110页

作者： Broad, Andrew Wright, Alexander I. de Kamps, Marc Treanor, Darren School of Computing University of Leeds Sir William Henry Bragg Building Woodhouse Lane UK Leeds LS2 9BW United Kingdom Leeds Institute for Data Analytics University of Leeds Level 11 Worsley Building Clarendon Way UK Leeds LS2 9NL United Kingdom Leeds Teaching Hospitals NHS Trust Beckett St Harehills UK Leeds LS9 7TF United Kingdom Division of Pathology and Data Analytics Leeds Institute of Medical Research University of Leeds St James's University Hospital UK Leeds LS9 7TF United Kingdom The Alan Turing Institute 96 Euston Road UK London NW1 2DB United Kingdom University of Leeds UK Leeds LS2 9JT United Kingdom Department of Clinical Pathology Department of Clinical and Experimental Medicine Linköping University Linköping Sweden Center for Medical Image Science and Visualization (CMIV) Linköping University Linköping Sweden

Improvements to patient care through the development of automated image analysis in pathology are restricted by the small image patch size that can be processed by convolutional neural networks (CNNs), when compared to the whole-slide image (WSI). Tile-by-tile processing across the entire WSI is slow and inefficient. While this may improve with future computing power, the technique remains vulnerable to noise from uninformative image areas. We propose a novel attention-inspired algorithm that selects image patches from informative parts of the WSI, first using a sparse randomised grid pattern, then iteratively re-sampling at higher density in regions where a CNN classifies patches as tumour. Subsequent uniform sampling across the enclosing region of interest (ROI) is used to mitigate sampling bias. Benchmarking tests informed the adoption of VGG19 as the main CNN architecture, with 79% classification accuracy. A further CNN was trained to separate false-positive normal epithelium from tumour epithelium, in a novel adaptation of a two-stage model used in brain imaging. These subsystems were combined in a processing pipeline to generate spatial distributions of classified patches from unseen WSIs. The ROI was predicted with a mean F1 (Dice) score of 86.6% over 100 evaluation WSIs. Several algorithms for evaluating tumour–stroma ratio (TSR) within the ROI were compared, giving a lowest root mean square (RMS) error of 11.3% relative to pathologists’ annotations, against 13.5% for an equivalent tile-by-tile pipeline. Our pipeline processed WSIs between 3.3x and 6.3x faster than tile-by-tile processing. We propose our attention-based sampling pipeline as a useful tool for pathology researchers, with the further potential for incorporating additional diagnostic calculations. © 2022 The Authors

关键词： Artificial intelligence Attention Colorectal cancer Region of interest Sampling Tumour–stroma ratio

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