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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Online Access: | http://hdl.handle.net/11573/1487858 https://doi.org/10.3390/app10228285 |
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ftunivromairis:oai:iris.uniroma1.it:11573/1487858 2024-04-21T08:09:56+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 http://hdl.handle.net/11573/1487858 https://doi.org/10.3390/app10228285 eng eng MDPI AG info:eu-repo/semantics/altIdentifier/wos/WOS:000594871500001 volume:10 issue:22 firstpage:1 lastpage:14 numberofpages:14 journal:APPLIED SCIENCES http://hdl.handle.net/11573/1487858 doi:10.3390/app10228285 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85096394211 info:eu-repo/semantics/openAccess Deep learning Medical image segmentation Oral carcinoma info:eu-repo/semantics/article 2020 ftunivromairis https://doi.org/10.3390/app10228285 2024-03-28T02:11:51Z 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 Sapienza Università di Roma: CINECA IRIS Applied Sciences 10 22 8285 |
institution |
Open Polar |
collection |
Sapienza Università di Roma: CINECA IRIS |
op_collection_id |
ftunivromairis |
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 |
publisher |
MDPI AG |
publishDate |
2020 |
url |
http://hdl.handle.net/11573/1487858 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 http://hdl.handle.net/11573/1487858 doi:10.3390/app10228285 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85096394211 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/app10228285 |
container_title |
Applied Sciences |
container_volume |
10 |
container_issue |
22 |
container_start_page |
8285 |
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1796951183794896896 |