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  1. Book ; Online ; E-Book: Deep Learning for Medical Image Analysis

    Zhou, S. Kevin / Greenspan, Hayit / Shen, Dinggang

    (The MICCAI Society Book Series)

    2024  

    Author's details S. Kevin Zhou, Hayit Greenspan, and Dinggang Shen, editors
    Series title The MICCAI Society Book Series
    Keywords Artificial intelligence/Medical applications ; Medical informatics
    Subject code 610.285
    Language English
    Size 1 online resource (544 pages)
    Edition Second edition.
    Publisher Academic Press
    Publishing place Kidlington, England
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 0-323-85888-0 ; 978-0-323-85888-5
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book ; Online ; Conference proceedings ; E-Book: Multimodal Learning for Clinical Decision Support

    Syeda-Mahmood, Tanveer / Li, Xiang / Madabhushi, Anant / Greenspan, Hayit / Li, Quanzheng / Leahy, Richard / Dong, Bin / Wang, Hongzhi

    11th International Workshop, ML-CDS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings

    (Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 13050)

    2021  

    Abstract: This book constitutes the refereed joint proceedings of the 11th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2021, held in conjunction with the 24th International Conference on Medical Imaging and Computer-Assisted ...

    Author's details edited by Tanveer Syeda-Mahmood, Xiang Li, Anant Madabhushi, Hayit Greenspan, Quanzheng Li, Richard Leahy, Bin Dong, Hongzhi Wang
    Series title Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 13050
    Abstract This book constitutes the refereed joint proceedings of the 11th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2021, held in conjunction with the 24th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2021, in Strasbourg, France, in October 2021. The workshop was held virtually due to the COVID-19 pandemic. The 10 full papers presented at ML-CDS 2021 were carefully reviewed and selected from numerous submissions. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.
    Keywords Image processing/Digital techniques ; Computer vision ; Machine learning ; Database management ; Social sciences/Data processing ; Computer Imaging, Vision, Pattern Recognition and Graphics ; Machine Learning ; Database Management ; Computer Application in Social and Behavioral Sciences
    Subject code 616.07540285
    Language English
    Size 1 online resource (125 pages)
    Edition 1st ed. 2021.
    Publisher Springer International Publishing ; Imprint: Springer
    Publishing place Cham
    Document type Book ; Online ; Conference proceedings ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 3-030-89847-4 ; 3-030-89846-6 ; 978-3-030-89847-2 ; 978-3-030-89846-5
    DOI 10.1007/978-3-030-89847-2
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Article: Editorial: Generative adversarial networks in cardiovascular research.

    Zhang, Qiang / Cukur, Tolga / Greenspan, Hayit / Yang, Guang

    Frontiers in cardiovascular medicine

    2023  Volume 10, Page(s) 1307812

    Language English
    Publishing date 2023-10-23
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2781496-8
    ISSN 2297-055X
    ISSN 2297-055X
    DOI 10.3389/fcvm.2023.1307812
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Conference proceedings: Medical content based retrieval for clinical decision support

    Greenspan, Hayit / Müller, Henning / Syeda-Mahmood, Tanveer

    third MICCAI International Workshop, MCBR-CDS 2012, Nice, France, October 1, 2012 ; revised selected papers

    (Lecture notes in computer science ; 7723)

    2013  

    Title variant Medical content-based retrieval for clinical decision support
    Event/congress MICCAI (15, 2012, Nizza)
    Author's details Hayit Greenspan ; Henning Müller ; Tanveer Syeda-Mahmood (ed.)
    Series title Lecture notes in computer science ; 7723
    Collection
    Keywords Bildgebendes Verfahren ; Bildbanksystem ; Information Retrieval ; Entscheidungsunterstützungssystem
    Subject EUS ; DSS ; Decision support system ; Entscheidungshilfesystem ; Entscheidungssystem ; Informationsretrieval ; Information ; Informationsrecherche ; Informationswiedergewinnung ; Retrieval ; Informationsrückgewinnung ; Informationsgewinnung ; Bilddatenbanksystem ; Bildgebendes Diagnoseverfahren ; Diagnostik ; Bilddiagnostik ; Bildgebende Methode ; Medical Imaging ; Medizinische Bildgebung ; Bildgebende Diagnostik ; Bildgebende Verfahren ; Imaging
    Subject code 651.504261
    Language English
    Size VIII, 144 S. : Ill., graph. Darst., 24 cm
    Publisher Springer
    Publishing place Heidelberg u.a.
    Publishing country Germany
    Document type Book ; Conference proceedings
    Note Includes bibliographical references
    HBZ-ID HT017590822
    ISBN 978-3-642-36677-2 ; 3-642-36677-5 ; 9783642366789 ; 3642366783
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2013 A 193 available for loan (reading room) Lesesaal EG

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  5. Article ; Online: A Self Supervised StyleGAN for Image Annotation and Classification With Extremely Limited Labels.

    Cohen Hochberg, Dana / Greenspan, Hayit / Giryes, Raja

    IEEE transactions on medical imaging

    2022  Volume 41, Issue 12, Page(s) 3509–3519

    Abstract: The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the high costs associated with labeling, resulting in degraded performances of ...

    Abstract The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the high costs associated with labeling, resulting in degraded performances of deep learning methods. Self-supervised learning is frequently adopted to mitigate the reliance on massive labeled datasets since it exploits unlabeled data to learn relevant feature representations. In this work, we propose SS-StyleGAN, a self-supervised approach for image annotation and classification suitable for extremely small annotated datasets. This novel framework adds self-supervision to the StyleGAN architecture by integrating an encoder that learns the embedding to the StyleGAN latent space, which is well-known for its disentangled properties. The learned latent space enables the smart selection of representatives from the data to be labeled for improved classification performance. We show that the proposed method attains strong classification results using small labeled datasets of sizes 50 and even 10. We demonstrate the superiority of our approach for the tasks of COVID-19 and liver tumor pathology identification.
    MeSH term(s) Humans ; Data Curation ; COVID-19 ; Algorithms ; Supervised Machine Learning
    Language English
    Publishing date 2022-12-02
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2022.3187170
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2546: 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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  6. Article: Quantification of Intra-Muscular Adipose Infiltration in Calf/Thigh MRI Using Fully and Weakly Supervised Semantic Segmentation.

    Amer, Rula / Nassar, Jannette / Trabelsi, Amira / Bendahan, David / Greenspan, Hayit / Ben-Eliezer, Noam

    Bioengineering (Basel, Switzerland)

    2022  Volume 9, Issue 7

    Abstract: Purpose: ...

    Abstract Purpose:
    Language English
    Publishing date 2022-07-14
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2746191-9
    ISSN 2306-5354
    ISSN 2306-5354
    DOI 10.3390/bioengineering9070315
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: A review of self-supervised, generative, and few-shot deep learning methods for data-limited magnetic resonance imaging segmentation.

    Liu, Zelong / Kainth, Komal / Zhou, Alexander / Deyer, Timothy W / Fayad, Zahi A / Greenspan, Hayit / Mei, Xueyan

    NMR in biomedicine

    2024  , Page(s) e5143

    Abstract: Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and ... ...

    Abstract Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical-level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state-of-the-art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self-supervised learning, generative models, few-shot learning, and semi-supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field-including emerging algorithms, such as contrastive language-image pretraining, and potential combinations across the methods discussed-that can further increase the efficacy of image segmentation with limited labels.
    Language English
    Publishing date 2024-03-24
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 1000976-0
    ISSN 1099-1492 ; 0952-3480
    ISSN (online) 1099-1492
    ISSN 0952-3480
    DOI 10.1002/nbm.5143
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2869: 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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  8. Article ; Online: Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset.

    Gozes, Ophir / Greenspan, Hayit

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2019  Volume 2019, Page(s) 4076–4079

    Abstract: The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly available in medical imaging tasks. The recent emergence of a large Chest X-ray dataset opened the ... ...

    Abstract The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly available in medical imaging tasks. The recent emergence of a large Chest X-ray dataset opened the possibility for learning features that are specific to the X-ray analysis task. In this work, we demonstrate that the features learned allow for better classification results for the problem of Tuberculosis detection and enable generalization to an unseen dataset.To accomplish the task of feature learning, we train a DenseNet-121 CNN on 112K images from the ChestXray14 dataset which includes labels of 14 common thoracic pathologies. In addition to the pathology labels, we incorporate meta-data which is available in the dataset: Patient Positioning, Gender and Patient Age. We term this architecture MetaChexNet. As a by-product of the feature learning, we demonstrate state of the art performance on the task of patient Age & Gender estimation using CNN's. Finally, we show the features learned using ChestXray14 allow for better transfer learning on small-scale datasets for Tuberculosis.
    MeSH term(s) Deep Learning ; Humans ; Neural Networks, Computer ; Radiography, Thoracic ; Tuberculosis/diagnostic imaging ; X-Rays
    Language English
    Publishing date 2019-12-30
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC.2019.8856729
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Estimation of subvoxel fat infiltration in neurodegenerative muscle disorders using quantitative multi-T

    Nassar, Jannette / Trabelsi, Amira / Amer, Rula / Le Fur, Yann / Attarian, Shahram / Radunsky, Dvir / Blumenfeld-Katzir, Tamar / Greenspan, Hayit / Bendahan, David / Ben-Eliezer, Noam

    NMR in biomedicine

    2023  , Page(s) e4947

    Abstract: ... MRI's ... ...

    Abstract MRI's T
    Language English
    Publishing date 2023-04-06
    Publishing country England
    Document type Journal Article
    ZDB-ID 1000976-0
    ISSN 1099-1492 ; 0952-3480
    ISSN (online) 1099-1492
    ISSN 0952-3480
    DOI 10.1002/nbm.4947
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2869: 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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  10. Book ; Online: Neural Network Reconstruction of the Left Atrium using Sparse Catheter Paths

    Baram, Alon / Safran, Moshe / Noy, Tomer / Geri, Naveh / Greenspan, Hayit

    2023  

    Abstract: Catheter based radiofrequency ablation for pulmonary vein isolation has become the first line of treatment for atrial fibrillation in recent years. This requires a rather accurate map of the left atrial sub-endocardial surface including the ostia of the ... ...

    Abstract Catheter based radiofrequency ablation for pulmonary vein isolation has become the first line of treatment for atrial fibrillation in recent years. This requires a rather accurate map of the left atrial sub-endocardial surface including the ostia of the pulmonary veins, which requires dense sampling of the surface and takes more than 10 minutes. The focus of this work is to provide left atrial visualization early in the procedure to ease procedure complexity and enable further workflows, such as using catheters that have difficulty sampling the surface. We propose a dense encoder-decoder network with a novel regularization term to reconstruct the shape of the left atrium from partial data which is derived from simple catheter maneuvers. To train the network, we acquire a large dataset of 3D atria shapes and generate corresponding catheter trajectories. Once trained, we show that the suggested network can sufficiently approximate the atrium shape based on a given trajectory. We compare several network solutions for the 3D atrium reconstruction. We demonstrate that the solution proposed produces realistic visualization using partial acquisition within a 3-minute time interval. Synthetic and human clinical cases are shown.

    Comment: 15 pages, 15 figures
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 000
    Publishing date 2023-11-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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