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  1. Artikel ; Online: Automated Wound Image Segmentation: Transfer Learning from Human to Pet via Active Semi-Supervised Learning

    Buschi, Daniele / Curti, Nico / Cola, Veronica / Carlini, Gianluca / Sala, Claudia / Dall’Olio, Daniele / Castellani, Gastone / Pizzi, Elisa / Del Magno, Sara / Foglia, Armando / Giunti, Massimo / Pisoni, Luciano / Giampieri, Enrico

    Animals. 2023 Mar. 07, v. 13, no. 6

    2023  

    Abstract: Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications regarding wound management for pets. Precise and efficient wound assessment is helpful to improve ... ...

    Abstract Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications regarding wound management for pets. Precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for chronic wounds. In this work, we introduced a novel pipeline for the segmentation of pet wound images. Starting from a model pre-trained on human-based wound images, we applied a combination of transfer learning (TL) and active semi-supervised learning (ASSL) to automatically label a large dataset. Additionally, we provided a guideline for future applications of TL+ASSL training strategy on image datasets. We compared the effectiveness of the proposed training strategy, monitoring the performance of an EfficientNet-b3 U-Net model against the lighter solution provided by a MobileNet-v2 U-Net model. We obtained 80% of correctly segmented images after five rounds of ASSL training. The EfficientNet-b3 U-Net model significantly outperformed the MobileNet-v2 one. We proved that the number of available samples is a key factor for the correct usage of ASSL training. The proposed approach is a viable solution to reduce the time required for the generation of a segmentation dataset.
    Schlagwörter automation ; data collection ; guidelines ; humans ; image analysis ; models ; wound treatment
    Sprache Englisch
    Erscheinungsverlauf 2023-0307
    Erscheinungsort Multidisciplinary Digital Publishing Institute
    Dokumenttyp Artikel ; Online
    ZDB-ID 2606558-7
    ISSN 2076-2615
    ISSN 2076-2615
    DOI 10.3390/ani13060956
    Datenquelle NAL Katalog (AGRICOLA)

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  2. Artikel: Automated Wound Image Segmentation: Transfer Learning from Human to Pet via Active Semi-Supervised Learning.

    Buschi, Daniele / Curti, Nico / Cola, Veronica / Carlini, Gianluca / Sala, Claudia / Dall'Olio, Daniele / Castellani, Gastone / Pizzi, Elisa / Del Magno, Sara / Foglia, Armando / Giunti, Massimo / Pisoni, Luciano / Giampieri, Enrico

    Animals : an open access journal from MDPI

    2023  Band 13, Heft 6

    Abstract: Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications regarding wound management for pets. Precise and efficient wound assessment is helpful to improve ... ...

    Abstract Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications regarding wound management for pets. Precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for chronic wounds. In this work, we introduced a novel pipeline for the segmentation of pet wound images. Starting from a model pre-trained on human-based wound images, we applied a combination of transfer learning (TL) and active semi-supervised learning (ASSL) to automatically label a large dataset. Additionally, we provided a guideline for future applications of TL+ASSL training strategy on image datasets. We compared the effectiveness of the proposed training strategy, monitoring the performance of an EfficientNet-b3 U-Net model against the lighter solution provided by a MobileNet-v2 U-Net model. We obtained 80% of correctly segmented images after five rounds of ASSL training. The EfficientNet-b3 U-Net model significantly outperformed the MobileNet-v2 one. We proved that the number of available samples is a key factor for the correct usage of ASSL training. The proposed approach is a viable solution to reduce the time required for the generation of a segmentation dataset.
    Sprache Englisch
    Erscheinungsdatum 2023-03-07
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2606558-7
    ISSN 2076-2615
    ISSN 2076-2615
    DOI 10.3390/ani13060956
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

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