Loading…
Determining the scale of image patches using a deep learning approach
Detecting the scale of histopathology images is important because it allows to exploit various sources of information to train deep learning (DL) models to recognise biological structures of interest. Large open access databases with images exist, such as The Cancer Genome Atlas (TCGA) and PubMed Ce...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Conference Proceeding |
| Language: | English |
| Subjects: | |
| Online Access: | Request full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | |
| container_end_page | 846 |
| container_issue | |
| container_start_page | 843 |
| container_title | |
| container_volume | |
| creator | Otalora, Sebastian Perdomo, Oscar Atzori, Manfredo Andersson, Mats Jacobsson, Ludwig Hedlund, Martin Muller, Henning |
| description | Detecting the scale of histopathology images is important because it allows to exploit various sources of information to train deep learning (DL) models to recognise biological structures of interest. Large open access databases with images exist, such as The Cancer Genome Atlas (TCGA) and PubMed Central but very few models can use such datasets because of the variability of the data in color and scale and a lack of metadata. In this article, we present and compare two deep learning architectures, to detect the scale of histopathology image patches. The approach is evaluated on a patch dataset from whole slide images of the prostate, obtaining a Cohen's kappa coefficient of 0.9897 in the classification of patches with a scale of 5×, 10× and 20×. The good results represent a first step towards magnification detection in histopathology images that can help to solve the problem on more heterogeneous data sources. |
| doi_str_mv | 10.1109/ISBI.2018.8363703 |
| format | conference_proceeding |
| fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_8363703</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8363703</ieee_id><sourcerecordid>8363703</sourcerecordid><originalsourceid>FETCH-LOGICAL-i218t-2830ce357b3c801ec4dedec89281801b06bbc1b8bbb2a40a618680a70d8ca9123</originalsourceid><addsrcrecordid>eNotT8tOwzAQNEhIVCUfgLj4BxJ2bcfeHKGUEqkSB-Bc2c62CeojisOBvydA5zKa0exqRohbhAIRqvv67bEuFCAVpK12oC9EVjnCUpOdDOsuxQwrU-ZkSnUtspQ-YYIzRoOZieUTjzwcumN33MmxZZmi37M8bWV38DuWvR9jy0l-pd-Alw1zL_fsh78D3_fDycf2Rlxt_T5xdua5-Hhevi9e8vXrql48rPNOIY25Ig2RdemCjgTI0TTccKRKEU46gA0hYqAQgvIGvEWyBN5BQ9FXqPRc3P3_7Zh50w9Tx-F7cx6ufwAK4Uuq</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Determining the scale of image patches using a deep learning approach</title><source>IEEE Xplore All Conference Series</source><creator>Otalora, Sebastian ; Perdomo, Oscar ; Atzori, Manfredo ; Andersson, Mats ; Jacobsson, Ludwig ; Hedlund, Martin ; Muller, Henning</creator><creatorcontrib>Otalora, Sebastian ; Perdomo, Oscar ; Atzori, Manfredo ; Andersson, Mats ; Jacobsson, Ludwig ; Hedlund, Martin ; Muller, Henning</creatorcontrib><description>Detecting the scale of histopathology images is important because it allows to exploit various sources of information to train deep learning (DL) models to recognise biological structures of interest. Large open access databases with images exist, such as The Cancer Genome Atlas (TCGA) and PubMed Central but very few models can use such datasets because of the variability of the data in color and scale and a lack of metadata. In this article, we present and compare two deep learning architectures, to detect the scale of histopathology image patches. The approach is evaluated on a patch dataset from whole slide images of the prostate, obtaining a Cohen's kappa coefficient of 0.9897 in the classification of patches with a scale of 5×, 10× and 20×. The good results represent a first step towards magnification detection in histopathology images that can help to solve the problem on more heterogeneous data sources.</description><identifier>EISSN: 1945-8452</identifier><identifier>EISBN: 9781538636367</identifier><identifier>EISBN: 1538636360</identifier><identifier>DOI: 10.1109/ISBI.2018.8363703</identifier><language>eng</language><publisher>IEEE</publisher><subject>Biological system modeling ; Biomedical imaging ; Biopsy ; Cancer ; Computer architecture ; Deep Learning ; DenseNet ; Digital Pathology ; Machine learning ; Magnification ; Prostate ; Scale ; Training</subject><ispartof>2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, p.843-846</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8363703$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,27906,54536,54913</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8363703$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Otalora, Sebastian</creatorcontrib><creatorcontrib>Perdomo, Oscar</creatorcontrib><creatorcontrib>Atzori, Manfredo</creatorcontrib><creatorcontrib>Andersson, Mats</creatorcontrib><creatorcontrib>Jacobsson, Ludwig</creatorcontrib><creatorcontrib>Hedlund, Martin</creatorcontrib><creatorcontrib>Muller, Henning</creatorcontrib><title>Determining the scale of image patches using a deep learning approach</title><title>2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)</title><addtitle>ISBI</addtitle><description>Detecting the scale of histopathology images is important because it allows to exploit various sources of information to train deep learning (DL) models to recognise biological structures of interest. Large open access databases with images exist, such as The Cancer Genome Atlas (TCGA) and PubMed Central but very few models can use such datasets because of the variability of the data in color and scale and a lack of metadata. In this article, we present and compare two deep learning architectures, to detect the scale of histopathology image patches. The approach is evaluated on a patch dataset from whole slide images of the prostate, obtaining a Cohen's kappa coefficient of 0.9897 in the classification of patches with a scale of 5×, 10× and 20×. The good results represent a first step towards magnification detection in histopathology images that can help to solve the problem on more heterogeneous data sources.</description><subject>Biological system modeling</subject><subject>Biomedical imaging</subject><subject>Biopsy</subject><subject>Cancer</subject><subject>Computer architecture</subject><subject>Deep Learning</subject><subject>DenseNet</subject><subject>Digital Pathology</subject><subject>Machine learning</subject><subject>Magnification</subject><subject>Prostate</subject><subject>Scale</subject><subject>Training</subject><issn>1945-8452</issn><isbn>9781538636367</isbn><isbn>1538636360</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2018</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotT8tOwzAQNEhIVCUfgLj4BxJ2bcfeHKGUEqkSB-Bc2c62CeojisOBvydA5zKa0exqRohbhAIRqvv67bEuFCAVpK12oC9EVjnCUpOdDOsuxQwrU-ZkSnUtspQ-YYIzRoOZieUTjzwcumN33MmxZZmi37M8bWV38DuWvR9jy0l-pd-Alw1zL_fsh78D3_fDycf2Rlxt_T5xdua5-Hhevi9e8vXrql48rPNOIY25Ig2RdemCjgTI0TTccKRKEU46gA0hYqAQgvIGvEWyBN5BQ9FXqPRc3P3_7Zh50w9Tx-F7cx6ufwAK4Uuq</recordid><startdate>20180523</startdate><enddate>20180523</enddate><creator>Otalora, Sebastian</creator><creator>Perdomo, Oscar</creator><creator>Atzori, Manfredo</creator><creator>Andersson, Mats</creator><creator>Jacobsson, Ludwig</creator><creator>Hedlund, Martin</creator><creator>Muller, Henning</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20180523</creationdate><title>Determining the scale of image patches using a deep learning approach</title><author>Otalora, Sebastian ; Perdomo, Oscar ; Atzori, Manfredo ; Andersson, Mats ; Jacobsson, Ludwig ; Hedlund, Martin ; Muller, Henning</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i218t-2830ce357b3c801ec4dedec89281801b06bbc1b8bbb2a40a618680a70d8ca9123</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Biological system modeling</topic><topic>Biomedical imaging</topic><topic>Biopsy</topic><topic>Cancer</topic><topic>Computer architecture</topic><topic>Deep Learning</topic><topic>DenseNet</topic><topic>Digital Pathology</topic><topic>Machine learning</topic><topic>Magnification</topic><topic>Prostate</topic><topic>Scale</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Otalora, Sebastian</creatorcontrib><creatorcontrib>Perdomo, Oscar</creatorcontrib><creatorcontrib>Atzori, Manfredo</creatorcontrib><creatorcontrib>Andersson, Mats</creatorcontrib><creatorcontrib>Jacobsson, Ludwig</creatorcontrib><creatorcontrib>Hedlund, Martin</creatorcontrib><creatorcontrib>Muller, Henning</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Otalora, Sebastian</au><au>Perdomo, Oscar</au><au>Atzori, Manfredo</au><au>Andersson, Mats</au><au>Jacobsson, Ludwig</au><au>Hedlund, Martin</au><au>Muller, Henning</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Determining the scale of image patches using a deep learning approach</atitle><btitle>2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)</btitle><stitle>ISBI</stitle><date>2018-05-23</date><risdate>2018</risdate><spage>843</spage><epage>846</epage><pages>843-846</pages><eissn>1945-8452</eissn><eisbn>9781538636367</eisbn><eisbn>1538636360</eisbn><abstract>Detecting the scale of histopathology images is important because it allows to exploit various sources of information to train deep learning (DL) models to recognise biological structures of interest. Large open access databases with images exist, such as The Cancer Genome Atlas (TCGA) and PubMed Central but very few models can use such datasets because of the variability of the data in color and scale and a lack of metadata. In this article, we present and compare two deep learning architectures, to detect the scale of histopathology image patches. The approach is evaluated on a patch dataset from whole slide images of the prostate, obtaining a Cohen's kappa coefficient of 0.9897 in the classification of patches with a scale of 5×, 10× and 20×. The good results represent a first step towards magnification detection in histopathology images that can help to solve the problem on more heterogeneous data sources.</abstract><pub>IEEE</pub><doi>10.1109/ISBI.2018.8363703</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 1945-8452 |
| ispartof | 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, p.843-846 |
| issn | 1945-8452 |
| language | eng |
| recordid | cdi_ieee_primary_8363703 |
| source | IEEE Xplore All Conference Series |
| subjects | Biological system modeling Biomedical imaging Biopsy Cancer Computer architecture Deep Learning DenseNet Digital Pathology Machine learning Magnification Prostate Scale Training |
| title | Determining the scale of image patches using a deep learning approach |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T01%3A14%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Determining%20the%20scale%20of%20image%20patches%20using%20a%20deep%20learning%20approach&rft.btitle=2018%20IEEE%2015th%20International%20Symposium%20on%20Biomedical%20Imaging%20(ISBI%202018)&rft.au=Otalora,%20Sebastian&rft.date=2018-05-23&rft.spage=843&rft.epage=846&rft.pages=843-846&rft.eissn=1945-8452&rft_id=info:doi/10.1109/ISBI.2018.8363703&rft.eisbn=9781538636367&rft.eisbn_list=1538636360&rft_dat=%3Cieee_CHZPO%3E8363703%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i218t-2830ce357b3c801ec4dedec89281801b06bbc1b8bbb2a40a618680a70d8ca9123%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=8363703&rfr_iscdi=true |