Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images
Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used...
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ftunivmoliseiris:oai:iris.unimol.it:11695/98182 2024-04-14T08:17:58+00:00 Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images Martino F. Bloisi D. D. Pennisi A. Fawakherji M. Ilardi G. Russo D. Nardi D. Staibano S. Merolla F. Martino, F. Bloisi, D. D. Pennisi, A. Fawakherji, M. Ilardi, G. Russo, D. Nardi, D. Staibano, S. Merolla, F. 2020 https://hdl.handle.net/11695/98182 https://doi.org/10.3390/app10228285 eng eng info:eu-repo/semantics/altIdentifier/wos/WOS:000594871500001 volume:10 issue:22 firstpage:1 lastpage:14 numberofpages:14 journal:APPLIED SCIENCES https://hdl.handle.net/11695/98182 doi:10.3390/app10228285 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85096394211 Deep learning Medical image segmentation Oral carcinoma info:eu-repo/semantics/article 2020 ftunivmoliseiris https://doi.org/10.3390/app10228285 2024-03-21T18:11:17Z Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset. Article in Journal/Newspaper Orca Università degli Studi del Molise: IRIS Applied Sciences 10 22 8285 |
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Open Polar |
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Università degli Studi del Molise: IRIS |
op_collection_id |
ftunivmoliseiris |
language |
English |
topic |
Deep learning Medical image segmentation Oral carcinoma |
spellingShingle |
Deep learning Medical image segmentation Oral carcinoma Martino F. Bloisi D. D. Pennisi A. Fawakherji M. Ilardi G. Russo D. Nardi D. Staibano S. Merolla F. Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images |
topic_facet |
Deep learning Medical image segmentation Oral carcinoma |
description |
Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset. |
author2 |
Martino, F. Bloisi, D. D. Pennisi, A. Fawakherji, M. Ilardi, G. Russo, D. Nardi, D. Staibano, S. Merolla, F. |
format |
Article in Journal/Newspaper |
author |
Martino F. Bloisi D. D. Pennisi A. Fawakherji M. Ilardi G. Russo D. Nardi D. Staibano S. Merolla F. |
author_facet |
Martino F. Bloisi D. D. Pennisi A. Fawakherji M. Ilardi G. Russo D. Nardi D. Staibano S. Merolla F. |
author_sort |
Martino F. |
title |
Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images |
title_short |
Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images |
title_full |
Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images |
title_fullStr |
Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images |
title_full_unstemmed |
Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images |
title_sort |
deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images |
publishDate |
2020 |
url |
https://hdl.handle.net/11695/98182 https://doi.org/10.3390/app10228285 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/WOS:000594871500001 volume:10 issue:22 firstpage:1 lastpage:14 numberofpages:14 journal:APPLIED SCIENCES https://hdl.handle.net/11695/98182 doi:10.3390/app10228285 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85096394211 |
op_doi |
https://doi.org/10.3390/app10228285 |
container_title |
Applied Sciences |
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10 |
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22 |
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8285 |
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