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dc.contributor.authorSingh, Divij
dc.contributor.authorSomani, Ayush
dc.contributor.authorHorsch, Alexander
dc.contributor.authorPrasad, Dilip K.
dc.date.accessioned2023-04-25T13:16:32Z
dc.date.available2023-04-25T13:16:32Z
dc.date.issued2022-04-26
dc.description.abstractSegmenting medical images accurately and reliably is crucial for disease diagnosis and treatment. Due to the wide assortment of objects’ sizes, shapes, and scanning modalities, it has become more challenging. Many convolutional neural networks (CNN) have recently been designed for segmentation tasks and achieved great success. This paper presents an optimized deep learning solution using DeepLabv3+ with ResNet-101 as its backbone. The proposed approach allows capturing variabilities of diverse objects. It provides improved and reliable quantitative and qualitative results in comparison to other state-of-the-art (SOTA) methods on two publicly available gastrointestinal and colonoscopy datasets. Few studies show the inadequacy of stable performance in varying object segmentation tasks, notwithstanding the sizes of objects. Our method has stable performance in the segmentation of large and small medical objects. The explainability of our robust model with benchmarking on SOTA approaches for both datasets will be fruitful for further research on biomedical image segmentation.en_US
dc.identifier.citationSingh D, Somani A, Horsch A, Prasad DK. Counterfactual Explainable Gastrointestinal and Colonoscopy Image Segmentation. IEEE International Symposium on Biomedical Imaging. 2022:1-5en_US
dc.identifier.cristinIDFRIDAID 2112019
dc.identifier.doi10.1109/ISBI52829.2022.9761664
dc.identifier.issn1945-7928
dc.identifier.issn1945-8452
dc.identifier.urihttps://hdl.handle.net/10037/29052
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE International Symposium on Biomedical Imaging
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleCounterfactual Explainable Gastrointestinal and Colonoscopy Image Segmentationen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)