J Biomed Inform - Automatic figure classification in bioscience literature.

Tópicos

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{ extract(1171) text(1153) clinic(932) }
{ imag(1947) propos(1133) code(1026) }
{ model(3480) simul(1196) paramet(876) }
{ result(1111) use(1088) new(759) }
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{ data(3963) clinic(1234) research(1004) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ age(1611) year(1155) adult(843) }
{ first(2504) two(1366) second(1323) }
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{ take(945) account(800) differ(722) }
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{ compound(1573) activ(1297) structur(1058) }
{ analysi(2126) use(1163) compon(1037) }
{ model(3404) distribut(989) bayesian(671) }
{ motion(1329) object(1292) video(1091) }
{ learn(2355) train(1041) set(1003) }
{ care(1570) inform(1187) nurs(1089) }
{ studi(1410) differ(1259) use(1210) }
{ monitor(1329) mobil(1314) devic(1160) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
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{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
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{ perform(999) metric(946) measur(919) }
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{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
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{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Millions of figures appear in biomedical articles, and it is important to develop an intelligent figure search engine to return relevant figures based on user entries. In this study we report a figure classifier that automatically classifies biomedical figures into five predefined figure types: Gel-image, Image-of-thing, Graph, Model, and Mix. The classifier explored rich image features and integrated them with text features. We performed feature selection and explored different classification models, including a rule-based figure classifier, a supervised machine-learning classifier, and a multi-model classifier, the latter of which integrated the first two classifiers. Our results show that feature selection improved figure classification and the novel image features we explored were the best among image features that we have examined. Our results also show that integrating text and image features achieved better performance than using either of them individually. The best system is a multi-model classifier which combines the rule-based hierarchical classifier and a support vector machine (SVM) based classifier, achieving a 76.7% F1-score for five-type classification. We demonstrated our system at http://figureclassification.askhermes.org/.

Resumo Limpo

million figur appear biomed articl import develop intellig figur search engin return relev figur base user entri studi report figur classifi automat classifi biomed figur five predefin figur type gelimag imageofth graph model mix classifi explor rich imag featur integr text featur perform featur select explor differ classif model includ rulebas figur classifi supervis machinelearn classifi multimodel classifi latter integr first two classifi result show featur select improv figur classif novel imag featur explor best among imag featur examin result also show integr text imag featur achiev better perform use either individu best system multimodel classifi combin rulebas hierarch classifi support vector machin svm base classifi achiev fscore fivetyp classif demonstr system httpfigureclassificationaskhermesorg

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