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  1. Book ; Online ; Conference proceedings ; E-Book: Myocardial pathology segmentation combining multi-sequence cardiac magnetic resonance images

    Zhuang, Xiahai / Li, Lei

    first challenge, MyoPS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings

    (Lecture notes in computer science ; 12554)

    2021  

    Abstract: This book constitutes the First Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge, MyoPS 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, ... ...

    Author's details Xiahai Zhuang, Lei Li (edsitors)
    Series title Lecture notes in computer science ; 12554
    Abstract This book constitutes the First Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge, MyoPS 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 crisis. The 12 full and 4 short papers presented in this volume were carefully reviewed and selected form numerous submissions. This challenge aims not only to benchmark various myocardial pathology segmentation algorithms, but also to cover the topic of general cardiac image segmentation, registration and modeling, and raise discussions for further technical development and clinical deployment.
    Keywords Diagnostic imaging/Data processing
    Subject code 616.07540285
    Language English
    Size 1 online resource (VIII, 177 p. 93 illus., 79 illus. in color.)
    Edition 1st ed. 2020.
    Publisher Springer
    Publishing place Cham, Switzerland
    Document type Book ; Online ; Conference proceedings ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 3-030-65651-9 ; 3-030-65650-0 ; 978-3-030-65651-5 ; 978-3-030-65650-8
    DOI 10.1007/978-3-030-65651-5
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article: Controlled Memristic Behavior of Metal-Organic Framework as a Promising Memory Device.

    Li, Lei

    Nanomaterials (Basel, Switzerland)

    2023  Volume 13, Issue 20

    Abstract: Metal-organic frameworks (MOFs) have attracted considerable interests for sensing, electrochemical, and catalytic applications. Most significantly, MOFs with highly accessible sites on their surface have promising potential for applications in high- ... ...

    Abstract Metal-organic frameworks (MOFs) have attracted considerable interests for sensing, electrochemical, and catalytic applications. Most significantly, MOFs with highly accessible sites on their surface have promising potential for applications in high-performance computing architecture. In this paper, Mg-MOF-74 (a MOF built of Mg(II) ions linked by 2,5-dioxido-1,4-benzenedicarboxylate (DOBDC) ligands) and graphene oxide composites (Mg-MOF-74@GO) were first used as an active layer to fabricate ternary memory devices. A comprehensive investigation of the multi-bit data storage performance for Mg-MOF-74@GO composites was discussed and summarized. Moreover, the structure change of Mg-MOF-74@GO after introducing GO was thoroughly studied. The as-fabricated resistive random access memory (RRAM) devices exhibit a ternary memristic behavior with low SET voltage, an R
    Language English
    Publishing date 2023-10-10
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662255-5
    ISSN 2079-4991
    ISSN 2079-4991
    DOI 10.3390/nano13202736
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Accessing hidden microbial biosynthetic potential from underexplored sources for novel drug discovery.

    Li, Lei

    Biotechnology advances

    2023  Volume 66, Page(s) 108176

    Abstract: Microbial natural products and their structural analogues have widely used as pharmaceutical agents, especially for infectious diseases and cancer. Despite this success, new structural classes with innovative chemistry and modes of action are urgently ... ...

    Abstract Microbial natural products and their structural analogues have widely used as pharmaceutical agents, especially for infectious diseases and cancer. Despite this success, new structural classes with innovative chemistry and modes of action are urgently needed to be developed to combat the growing antimicrobial resistance and other public health problems. The advances in next-generation sequencing technologies and powerful computational tools open up new opportunities to explore microbial biosynthetic potential from underexplored sources, with millions of secondary metabolites awaiting discovery. The review highlights challenges associated with discovery of new chemical entities, rich reservoirs provided by untapped taxa, ecological niches or host microbiomes, emerging synthetic biotechnologies to unearth the hidden microbial biosynthetic potential for novel drug discovery at scale and speed.
    MeSH term(s) Drug Discovery ; Biological Products/pharmacology ; Biological Products/chemistry
    Chemical Substances Biological Products
    Language English
    Publishing date 2023-05-19
    Publishing country England
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 47165-3
    ISSN 1873-1899 ; 0734-9750
    ISSN (online) 1873-1899
    ISSN 0734-9750
    DOI 10.1016/j.biotechadv.2023.108176
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3730: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article: Multi-Bit Biomemristic Behavior for Neutral Polysaccharide Dextran Blended with Chitosan.

    Li, Lei

    Nanomaterials (Basel, Switzerland)

    2022  Volume 12, Issue 7

    Abstract: Natural biomaterials applicable for biomemristors have drawn prominent attention and are of benefit to sustainability, biodegradability, biocompatibility, and metabolism. In this work, multi-bit biomemristors based on the neutral polysaccharide dextran ... ...

    Abstract Natural biomaterials applicable for biomemristors have drawn prominent attention and are of benefit to sustainability, biodegradability, biocompatibility, and metabolism. In this work, multi-bit biomemristors based on the neutral polysaccharide dextran were built using the spin-casting method, which was also employed to explore the effect of dextran on the ternary biomemristic behaviors of dextran-chitosan nanocomposites. The doping of 50 wt% dextran onto the bio-nanocomposite optimized the ratio of biomemristance in high-, intermediate-, and low-resistance states (10
    Language English
    Publishing date 2022-03-24
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662255-5
    ISSN 2079-4991
    ISSN 2079-4991
    DOI 10.3390/nano12071072
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  5. Article ; Online: Improved Feature Pyramid Convolutional Neural Network for Effective Recognition of Music Scores.

    Li, Lei

    Computational intelligence and neuroscience

    2022  Volume 2022, Page(s) 6071114

    Abstract: Music written by composers and performed by multidimensional instruments is an art form that reflects real-life emotions. Historically, people disseminated music primarily through sheet music recording and oral transmission. Among them, recording music ... ...

    Abstract Music written by composers and performed by multidimensional instruments is an art form that reflects real-life emotions. Historically, people disseminated music primarily through sheet music recording and oral transmission. Among them, recording music in sheet music form was a great musical invention. It became the carrier of music communication and inheritance, as well as a record of humanity's magnificent music culture. The advent of digital technology solves the problem of difficult musical score storage and distribution. However, there are many drawbacks to using data in image format, and extracting music score information in editable form from image data is currently a challenge. An improved convolutional neural network for musical score recognition is proposed in this paper. Because the traditional convolutional neural network SEGNET misclassifies some pixels, this paper employs the feature pyramid structure. Use additional branch paths to fuse shallow image details, shallow texture features that are beneficial to small objects, and high-level features of global information, enrich the multi-scale semantic information of the model, and alleviate the problem of the lack of multiscale semantic information in the model. Poor recognition performance is caused by semantic information. By comparing the recognition effects of other models, the experimental results show that the proposed musical score recognition model has a higher recognition accuracy and a stronger generalization performance. The improved generalization performance allows the musical score recognition method to be applied to more types of musical score recognition scenarios, and such a recognition model has more practical value.
    MeSH term(s) Emotions ; Humans ; Music/psychology ; Neural Networks, Computer ; Recognition, Psychology ; Semantics
    Language English
    Publishing date 2022-05-09
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2388208-6
    ISSN 1687-5273 ; 1687-5273
    ISSN (online) 1687-5273
    ISSN 1687-5273
    DOI 10.1155/2022/6071114
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: My journey studying plant immunity.

    Li, Lei

    Cell host & microbe

    2022  Volume 30, Issue 4, Page(s) 463–465

    Abstract: Our understanding of plant immunity has taken exciting and surprising turns over the past two decades. Here, I look back on my scientific journey studying plant immunity with three publications in Cell Host &Microbe, which have provided me perspectives ... ...

    Abstract Our understanding of plant immunity has taken exciting and surprising turns over the past two decades. Here, I look back on my scientific journey studying plant immunity with three publications in Cell Host &Microbe, which have provided me perspectives on future research in the area of plant-pathogen interactions.
    MeSH term(s) Plant Immunity
    Language English
    Publishing date 2022-04-07
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2278004-X
    ISSN 1934-6069 ; 1931-3128
    ISSN (online) 1934-6069
    ISSN 1931-3128
    DOI 10.1016/j.chom.2022.03.009
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6578: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  7. Book ; Conference proceedings ; Thesis: Metastasierungs- und Rezidivverhalten von plattenepithelialen Unterlippenkarzinomen

    Schieren, Ingmar Bernd Richard / Betz, Christian Stephan / Li, Lei

    eine retrospektive klinische Studie anhand eines Patientenkollektivs

    2020  

    Institution Universität Hamburg
    Event/congress Universität Hamburg (MedizinischeFakultät)
    Author's details vorgelegt von: Ingmar Bernd Richard Schieren ; Prüfungsausschuss, der Vorsitzende: Prof. Dr. Christian Betz, Prüfungsausschuss, zweiter Gutachter: PD Dr. Dr. Lei Li
    Subject code 610
    Language German
    Size 138 Blätter, Illustrationen, Diagramme, 30 cm
    Publishing place Hamburg
    Publishing country Germany
    Document type Book ; Conference proceedings ; Thesis
    Thesis / German Habilitation thesis Dissertation, Universität Hamburg, 2021
    HBZ-ID HT021060513
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2021 E 1187 order for loan Magazin

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  8. Book ; Online: CPSeg

    Li, Lei

    Finer-grained Image Semantic Segmentation via Chain-of-Thought Language Prompting

    2023  

    Abstract: Natural scene analysis and remote sensing imagery offer immense potential for advancements in large-scale language-guided context-aware data utilization. This potential is particularly significant for enhancing performance in downstream tasks such as ... ...

    Abstract Natural scene analysis and remote sensing imagery offer immense potential for advancements in large-scale language-guided context-aware data utilization. This potential is particularly significant for enhancing performance in downstream tasks such as object detection and segmentation with designed language prompting. In light of this, we introduce the CPSeg, Chain-of-Thought Language Prompting for Finer-grained Semantic Segmentation), an innovative framework designed to augment image segmentation performance by integrating a novel "Chain-of-Thought" process that harnesses textual information associated with images. This groundbreaking approach has been applied to a flood disaster scenario. CPSeg encodes prompt texts derived from various sentences to formulate a coherent chain-of-thought. We propose a new vision-language dataset, FloodPrompt, which includes images, semantic masks, and corresponding text information. This not only strengthens the semantic understanding of the scenario but also aids in the key task of semantic segmentation through an interplay of pixel and text matching maps. Our qualitative and quantitative analyses validate the effectiveness of CPSeg.

    Comment: WACV 2024
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-10-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Edge Aware Learning for 3D Point Cloud

    Li, Lei

    2023  

    Abstract: This paper proposes an innovative approach to Hierarchical Edge Aware 3D Point Cloud Learning (HEA-Net) that seeks to address the challenges of noise in point cloud data, and improve object recognition and segmentation by focusing on edge features. In ... ...

    Abstract This paper proposes an innovative approach to Hierarchical Edge Aware 3D Point Cloud Learning (HEA-Net) that seeks to address the challenges of noise in point cloud data, and improve object recognition and segmentation by focusing on edge features. In this study, we present an innovative edge-aware learning methodology, specifically designed to enhance point cloud classification and segmentation. Drawing inspiration from the human visual system, the concept of edge-awareness has been incorporated into this methodology, contributing to improved object recognition while simultaneously reducing computational time. Our research has led to the development of an advanced 3D point cloud learning framework that effectively manages object classification and segmentation tasks. A unique fusion of local and global network learning paradigms has been employed, enriched by edge-focused local and global embeddings, thereby significantly augmenting the model's interpretative prowess. Further, we have applied a hierarchical transformer architecture to boost point cloud processing efficiency, thus providing nuanced insights into structural understanding. Our approach demonstrates significant promise in managing noisy point cloud data and highlights the potential of edge-aware strategies in 3D point cloud learning. The proposed approach is shown to outperform existing techniques in object classification and segmentation tasks, as demonstrated by experiments on ModelNet40 and ShapeNet datasets.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006 ; 004
    Publishing date 2023-09-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Segment Any Building For Remote Sensing

    Li, Lei

    2023  

    Abstract: The task of identifying and segmenting buildings within remote sensing imagery has perennially stood at the forefront of scholarly investigations. This manuscript accentuates the potency of harnessing diversified datasets in tandem with cutting-edge ... ...

    Abstract The task of identifying and segmenting buildings within remote sensing imagery has perennially stood at the forefront of scholarly investigations. This manuscript accentuates the potency of harnessing diversified datasets in tandem with cutting-edge representation learning paradigms for building segmentation in such images. Through the strategic amalgamation of disparate datasets, we have not only expanded the informational horizon accessible for model training but also manifested unparalleled performance metrics across multiple datasets. Our avant-garde joint training regimen underscores the merit of our approach, bearing significant implications in pivotal domains such as urban infrastructural development, disaster mitigation strategies, and ecological surveillance. Our methodology, predicated upon the fusion of datasets and gleaning insights from pre-trained models, carves a new benchmark in the annals of building segmentation endeavors. The outcomes of this research both fortify the foundations for ensuing scholarly pursuits and presage a horizon replete with innovative applications in the discipline of building segmentation.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2023-10-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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