Convolutional neural networks for automatic detection of Focal Cortical Dysplasia
Focal cortical dysplasia (FCD) is one of the most common epileptogenic lesions associated with cortical development malformations. However, the accurate detection of the FCD relies on the radiologist professionalism, and in many cases, the lesion could be missed. In this work, we solve the problem o...
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creator | Aliev, Ruslan Kondrateva, Ekaterina Sharaev, Maxim Bronov, Oleg Marinets, Alexey Subbotin, Sergey Bernstein, Alexander Burnaev, Evgeny |
description | Focal cortical dysplasia (FCD) is one of the most common epileptogenic
lesions associated with cortical development malformations. However, the
accurate detection of the FCD relies on the radiologist professionalism, and in
many cases, the lesion could be missed. In this work, we solve the problem of
automatic identification of FCD on magnetic resonance images (MRI). For this
task, we improve recent methods of Deep Learning-based FCD detection and apply
it for a dataset of 15 labeled FCD patients. The model results in the
successful detection of FCD on 11 out of 15 subjects. |
doi_str_mv | 10.48550/arxiv.2010.10373 |
format | Article |
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lesions associated with cortical development malformations. However, the
accurate detection of the FCD relies on the radiologist professionalism, and in
many cases, the lesion could be missed. In this work, we solve the problem of
automatic identification of FCD on magnetic resonance images (MRI). For this
task, we improve recent methods of Deep Learning-based FCD detection and apply
it for a dataset of 15 labeled FCD patients. The model results in the
successful detection of FCD on 11 out of 15 subjects.</description><identifier>DOI: 10.48550/arxiv.2010.10373</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2020-10</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,781,886</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2010.10373$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2010.10373$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Aliev, Ruslan</creatorcontrib><creatorcontrib>Kondrateva, Ekaterina</creatorcontrib><creatorcontrib>Sharaev, Maxim</creatorcontrib><creatorcontrib>Bronov, Oleg</creatorcontrib><creatorcontrib>Marinets, Alexey</creatorcontrib><creatorcontrib>Subbotin, Sergey</creatorcontrib><creatorcontrib>Bernstein, Alexander</creatorcontrib><creatorcontrib>Burnaev, Evgeny</creatorcontrib><title>Convolutional neural networks for automatic detection of Focal Cortical Dysplasia</title><description>Focal cortical dysplasia (FCD) is one of the most common epileptogenic
lesions associated with cortical development malformations. However, the
accurate detection of the FCD relies on the radiologist professionalism, and in
many cases, the lesion could be missed. In this work, we solve the problem of
automatic identification of FCD on magnetic resonance images (MRI). For this
task, we improve recent methods of Deep Learning-based FCD detection and apply
it for a dataset of 15 labeled FCD patients. The model results in the
successful detection of FCD on 11 out of 15 subjects.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj01OwzAUhL1hgQoHYIUvkGL72U68RIEWpEoVUvfR80-kiDSuHKfQ2zcNrGY0MxrpI-SJs7WslGIvmH6781qwOeAMSrgnX3UczrGfchcH7OkQprRI_onpe6RtTBSnHI-YO0d9yMHdljS2dBPdvKxjmpvZvF3GU49jhw_krsV-DI__uiKHzfuh_ih2--1n_borUJdQgAZbeuU4B4ZCm4DCtlahgUoK1NZjZZTn1qsqBKGVN8FIzlAaD9pJCSvy_He7MDWn1B0xXZobW7OwwRU_i0qt</recordid><startdate>20201020</startdate><enddate>20201020</enddate><creator>Aliev, Ruslan</creator><creator>Kondrateva, Ekaterina</creator><creator>Sharaev, Maxim</creator><creator>Bronov, Oleg</creator><creator>Marinets, Alexey</creator><creator>Subbotin, Sergey</creator><creator>Bernstein, Alexander</creator><creator>Burnaev, Evgeny</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20201020</creationdate><title>Convolutional neural networks for automatic detection of Focal Cortical Dysplasia</title><author>Aliev, Ruslan ; Kondrateva, Ekaterina ; Sharaev, Maxim ; Bronov, Oleg ; Marinets, Alexey ; Subbotin, Sergey ; Bernstein, Alexander ; Burnaev, Evgeny</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a673-363b7d5c1130a269ea2bfb5a93842a6bda895d1bd58ee265d9e9410a49d36c443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Aliev, Ruslan</creatorcontrib><creatorcontrib>Kondrateva, Ekaterina</creatorcontrib><creatorcontrib>Sharaev, Maxim</creatorcontrib><creatorcontrib>Bronov, Oleg</creatorcontrib><creatorcontrib>Marinets, Alexey</creatorcontrib><creatorcontrib>Subbotin, Sergey</creatorcontrib><creatorcontrib>Bernstein, Alexander</creatorcontrib><creatorcontrib>Burnaev, Evgeny</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Aliev, Ruslan</au><au>Kondrateva, Ekaterina</au><au>Sharaev, Maxim</au><au>Bronov, Oleg</au><au>Marinets, Alexey</au><au>Subbotin, Sergey</au><au>Bernstein, Alexander</au><au>Burnaev, Evgeny</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Convolutional neural networks for automatic detection of Focal Cortical Dysplasia</atitle><date>2020-10-20</date><risdate>2020</risdate><abstract>Focal cortical dysplasia (FCD) is one of the most common epileptogenic
lesions associated with cortical development malformations. However, the
accurate detection of the FCD relies on the radiologist professionalism, and in
many cases, the lesion could be missed. In this work, we solve the problem of
automatic identification of FCD on magnetic resonance images (MRI). For this
task, we improve recent methods of Deep Learning-based FCD detection and apply
it for a dataset of 15 labeled FCD patients. The model results in the
successful detection of FCD on 11 out of 15 subjects.</abstract><doi>10.48550/arxiv.2010.10373</doi><oa>free_for_read</oa></addata></record> |
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title | Convolutional neural networks for automatic detection of Focal Cortical Dysplasia |
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