Liver pathological image sample enhancement method based on random transformation

The invention relates to a liver pathological image sample enhancement method based on random transformation, which comprises the following steps: 1) carrying out block division on a liver pathological image to obtain a plurality of image small blocks; 2) performing random transformation on each ima...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: LU CHANGBIN, CHEN LIQING, YE MINGLI, LIN LONGJIANG, LEI XIAOYE, DOU KANGYIN
Format: Patent
Sprache:chi ; eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator LU CHANGBIN
CHEN LIQING
YE MINGLI
LIN LONGJIANG
LEI XIAOYE
DOU KANGYIN
description The invention relates to a liver pathological image sample enhancement method based on random transformation, which comprises the following steps: 1) carrying out block division on a liver pathological image to obtain a plurality of image small blocks; 2) performing random transformation on each image small block to form an extended sample; wherein the random transformation comprises more than one of horizontal mirror image overturning, vertical mirror image overturning, cutting, brightness adjustment, saturation adjustment and hue adjustment; and 3) inputting the extended sample into a deep learning model to train the liver pathological image, and performing corresponding enhancement on the liver pathological image to obtain an enhanced sample of the liver pathological image. According to the method, original pathological samples can be effectively expanded, the problems of insufficient sample number and non-uniform sample distribution are solved to a certain extent, the requirement of a large sample size of
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN110288542A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN110288542A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN110288542A3</originalsourceid><addsrcrecordid>eNqNyrEKwjAQBuAsDqK-w_kAgq0KXaUoDiII7uVM_7aB5C4kwee3gw_g9C3f0jzv7oNEkcukXkdn2ZMLPIIyh-hBkInFIkAKBcyrpzdn9KRCiaXXQGU2D5oCF6eyNouBfcbm58psr5dXe9shaocc2UJQuvZRVfu6aU7H-nz453wBECQ4Lw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Liver pathological image sample enhancement method based on random transformation</title><source>esp@cenet</source><creator>LU CHANGBIN ; CHEN LIQING ; YE MINGLI ; LIN LONGJIANG ; LEI XIAOYE ; DOU KANGYIN</creator><creatorcontrib>LU CHANGBIN ; CHEN LIQING ; YE MINGLI ; LIN LONGJIANG ; LEI XIAOYE ; DOU KANGYIN</creatorcontrib><description>The invention relates to a liver pathological image sample enhancement method based on random transformation, which comprises the following steps: 1) carrying out block division on a liver pathological image to obtain a plurality of image small blocks; 2) performing random transformation on each image small block to form an extended sample; wherein the random transformation comprises more than one of horizontal mirror image overturning, vertical mirror image overturning, cutting, brightness adjustment, saturation adjustment and hue adjustment; and 3) inputting the extended sample into a deep learning model to train the liver pathological image, and performing corresponding enhancement on the liver pathological image to obtain an enhanced sample of the liver pathological image. According to the method, original pathological samples can be effectively expanded, the problems of insufficient sample number and non-uniform sample distribution are solved to a certain extent, the requirement of a large sample size of</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PHYSICS</subject><creationdate>2019</creationdate><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://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20190927&amp;DB=EPODOC&amp;CC=CN&amp;NR=110288542A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20190927&amp;DB=EPODOC&amp;CC=CN&amp;NR=110288542A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LU CHANGBIN</creatorcontrib><creatorcontrib>CHEN LIQING</creatorcontrib><creatorcontrib>YE MINGLI</creatorcontrib><creatorcontrib>LIN LONGJIANG</creatorcontrib><creatorcontrib>LEI XIAOYE</creatorcontrib><creatorcontrib>DOU KANGYIN</creatorcontrib><title>Liver pathological image sample enhancement method based on random transformation</title><description>The invention relates to a liver pathological image sample enhancement method based on random transformation, which comprises the following steps: 1) carrying out block division on a liver pathological image to obtain a plurality of image small blocks; 2) performing random transformation on each image small block to form an extended sample; wherein the random transformation comprises more than one of horizontal mirror image overturning, vertical mirror image overturning, cutting, brightness adjustment, saturation adjustment and hue adjustment; and 3) inputting the extended sample into a deep learning model to train the liver pathological image, and performing corresponding enhancement on the liver pathological image to obtain an enhanced sample of the liver pathological image. According to the method, original pathological samples can be effectively expanded, the problems of insufficient sample number and non-uniform sample distribution are solved to a certain extent, the requirement of a large sample size of</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2019</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyrEKwjAQBuAsDqK-w_kAgq0KXaUoDiII7uVM_7aB5C4kwee3gw_g9C3f0jzv7oNEkcukXkdn2ZMLPIIyh-hBkInFIkAKBcyrpzdn9KRCiaXXQGU2D5oCF6eyNouBfcbm58psr5dXe9shaocc2UJQuvZRVfu6aU7H-nz453wBECQ4Lw</recordid><startdate>20190927</startdate><enddate>20190927</enddate><creator>LU CHANGBIN</creator><creator>CHEN LIQING</creator><creator>YE MINGLI</creator><creator>LIN LONGJIANG</creator><creator>LEI XIAOYE</creator><creator>DOU KANGYIN</creator><scope>EVB</scope></search><sort><creationdate>20190927</creationdate><title>Liver pathological image sample enhancement method based on random transformation</title><author>LU CHANGBIN ; CHEN LIQING ; YE MINGLI ; LIN LONGJIANG ; LEI XIAOYE ; DOU KANGYIN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN110288542A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2019</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>LU CHANGBIN</creatorcontrib><creatorcontrib>CHEN LIQING</creatorcontrib><creatorcontrib>YE MINGLI</creatorcontrib><creatorcontrib>LIN LONGJIANG</creatorcontrib><creatorcontrib>LEI XIAOYE</creatorcontrib><creatorcontrib>DOU KANGYIN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LU CHANGBIN</au><au>CHEN LIQING</au><au>YE MINGLI</au><au>LIN LONGJIANG</au><au>LEI XIAOYE</au><au>DOU KANGYIN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Liver pathological image sample enhancement method based on random transformation</title><date>2019-09-27</date><risdate>2019</risdate><abstract>The invention relates to a liver pathological image sample enhancement method based on random transformation, which comprises the following steps: 1) carrying out block division on a liver pathological image to obtain a plurality of image small blocks; 2) performing random transformation on each image small block to form an extended sample; wherein the random transformation comprises more than one of horizontal mirror image overturning, vertical mirror image overturning, cutting, brightness adjustment, saturation adjustment and hue adjustment; and 3) inputting the extended sample into a deep learning model to train the liver pathological image, and performing corresponding enhancement on the liver pathological image to obtain an enhanced sample of the liver pathological image. According to the method, original pathological samples can be effectively expanded, the problems of insufficient sample number and non-uniform sample distribution are solved to a certain extent, the requirement of a large sample size of</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN110288542A
source esp@cenet
subjects CALCULATING
COMPUTING
COUNTING
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
title Liver pathological image sample enhancement method based on random transformation
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T15%3A24%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=LU%20CHANGBIN&rft.date=2019-09-27&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN110288542A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true