CanSuR: a robust method for staining pattern recognition of HEp-2 cell IIF images View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-18

AUTHORS

Ankita Mandal, Pradipta Maji

ABSTRACT

The recognition of staining patterns present in human epithelial type 2 (HEp-2) cells helps to diagnose connective tissue disease. In this context, the paper introduces a robust method, termed as CanSuR, for automatic recognition of antinuclear autoantibodies by HEp-2 cell indirect immunofluorescence (IIF) image analysis. The proposed method combines the advantages of a new sequential supervised canonical correlation analysis (CCA), introduced in this paper, with the theory of rough hypercuboid approach. While the proposed CCA efficiently combines the local textural information of HEp-2 cells, derived from various scales of rotation-invariant local binary patterns, the relevant and significant features of HEp-2 cell for staining pattern recognition are extracted using rough hypercuboid approach. Finally, the support vector machine, with radial basis function kernel, is used to recognize one of the known staining patterns present in IIF images. The effectiveness of the proposed staining pattern recognition method, along with a comparison with related approaches, is demonstrated on MIVIA, SNP and ICPR HEp-2 cell image databases. An important finding is that the proposed method performs significantly better than state-of-the art methods, on three HEp-2 cell image databases with respect to both classification accuracy and F1 score. More... »

PAGES

1-19

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00521-019-04108-w

DOI

http://dx.doi.org/10.1007/s00521-019-04108-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112853830


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Indian Statistical Institute", 
          "id": "https://www.grid.ac/institutes/grid.39953.35", 
          "name": [
            "Biomedical Imaging and Bioinformatics Lab, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mandal", 
        "givenName": "Ankita", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Indian Statistical Institute", 
          "id": "https://www.grid.ac/institutes/grid.39953.35", 
          "name": [
            "Biomedical Imaging and Bioinformatics Lab, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Maji", 
        "givenName": "Pradipta", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/1471-2105-12-483", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001580986", 
          "https://doi.org/10.1186/1471-2105-12-483"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/art.10561", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008555084"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0065-2776(08)60641-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015609776"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/03009740500202664", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018572099"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0266-5611/11/3/007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024596623"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csda.2013.09.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024905613"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.autrev.2009.02.033", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025285970"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.asoc.2016.03.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026707550"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-2440-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027312764", 
          "https://doi.org/10.1007/978-1-4757-2440-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-2440-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027312764", 
          "https://doi.org/10.1007/978-1-4757-2440-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-4076(76)90010-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030912135"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-25346-1_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032660039", 
          "https://doi.org/10.1007/978-3-642-25346-1_8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neucom.2005.12.126", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038265102"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patcog.2013.09.026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038289541"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0031-3203(00)00010-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039662290"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jaut.2009.08.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040755997"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patrec.2016.02.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041045850"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patcog.2013.09.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045132756"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patcog.2013.09.024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047370072"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/art.30084", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049116187"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/cyto.a.20224", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052578672"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/cyto.a.20224", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052578672"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/annrheumdis-2013-203863", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053373773"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jbhi.2015.2508938", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061277204"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jbhi.2016.2526603", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061277224"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2016.2624823", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061530339"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2010.2044957", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061642447"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tkde.2012.242", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061662608"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2013.2268163", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061696130"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2002.1017623", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061742396"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/0106027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062837648"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2333955", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069895403"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1075817644", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2017.2672702", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083937086"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcyb.2017.2685625", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084605586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1504/ijdmb.2017.085713", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091134660"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ins.2017.08.083", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091396825"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cbms.2006.21", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093334414"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cbms.2006.21", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093334414"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cbms.2006.53", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093351997"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cbms.2006.53", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093351997"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/wacv.2013.6475005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093431445"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/bibe.2012.6399750", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093520640"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cbms.2010.6042611", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093774591"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icpr.1994.576366", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094758643"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cbms.2011.5999110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094877441"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icics.2009.5397624", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095348187"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-03-18", 
    "datePublishedReg": "2019-03-18", 
    "description": "The recognition of staining patterns present in human epithelial type 2 (HEp-2) cells helps to diagnose connective tissue disease. In this context, the paper introduces a robust method, termed as CanSuR, for automatic recognition of antinuclear autoantibodies by HEp-2 cell indirect immunofluorescence (IIF) image analysis. The proposed method combines the advantages of a new sequential supervised canonical correlation analysis (CCA), introduced in this paper, with the theory of rough hypercuboid approach. While the proposed CCA efficiently combines the local textural information of HEp-2 cells, derived from various scales of rotation-invariant local binary patterns, the relevant and significant features of HEp-2 cell for staining pattern recognition are extracted using rough hypercuboid approach. Finally, the support vector machine, with radial basis function kernel, is used to recognize one of the known staining patterns present in IIF images. The effectiveness of the proposed staining pattern recognition method, along with a comparison with related approaches, is demonstrated on MIVIA, SNP and ICPR HEp-2 cell image databases. An important finding is that the proposed method performs significantly better than state-of-the art methods, on three HEp-2 cell image databases with respect to both classification accuracy and F1 score.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00521-019-04108-w", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1104357", 
        "issn": [
          "0941-0643", 
          "1433-3058"
        ], 
        "name": "Neural Computing and Applications", 
        "type": "Periodical"
      }
    ], 
    "name": "CanSuR: a robust method for staining pattern recognition of HEp-2 cell IIF images", 
    "pagination": "1-19", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "7374b2d4edaeb532ae05d07df208ca3d4b19eb0f88da207c85788af85c390cf2"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00521-019-04108-w"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112853830"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00521-019-04108-w", 
      "https://app.dimensions.ai/details/publication/pub.1112853830"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:13", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000361_0000000361/records_53998_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00521-019-04108-w"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00521-019-04108-w'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00521-019-04108-w'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00521-019-04108-w'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00521-019-04108-w'


 

This table displays all metadata directly associated to this object as RDF triples.

191 TRIPLES      21 PREDICATES      67 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00521-019-04108-w schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N2d8a0a91e202419ca90c723a708be0b4
4 schema:citation sg:pub.10.1007/978-1-4757-2440-0
5 sg:pub.10.1007/978-3-642-25346-1_8
6 sg:pub.10.1186/1471-2105-12-483
7 https://app.dimensions.ai/details/publication/pub.1075817644
8 https://doi.org/10.1002/art.10561
9 https://doi.org/10.1002/art.30084
10 https://doi.org/10.1002/cyto.a.20224
11 https://doi.org/10.1016/0304-4076(76)90010-5
12 https://doi.org/10.1016/j.asoc.2016.03.010
13 https://doi.org/10.1016/j.autrev.2009.02.033
14 https://doi.org/10.1016/j.csda.2013.09.020
15 https://doi.org/10.1016/j.ins.2017.08.083
16 https://doi.org/10.1016/j.jaut.2009.08.009
17 https://doi.org/10.1016/j.neucom.2005.12.126
18 https://doi.org/10.1016/j.patcog.2013.09.018
19 https://doi.org/10.1016/j.patcog.2013.09.024
20 https://doi.org/10.1016/j.patcog.2013.09.026
21 https://doi.org/10.1016/j.patrec.2016.02.007
22 https://doi.org/10.1016/s0031-3203(00)00010-8
23 https://doi.org/10.1016/s0065-2776(08)60641-0
24 https://doi.org/10.1080/03009740500202664
25 https://doi.org/10.1088/0266-5611/11/3/007
26 https://doi.org/10.1109/bibe.2012.6399750
27 https://doi.org/10.1109/cbms.2006.21
28 https://doi.org/10.1109/cbms.2006.53
29 https://doi.org/10.1109/cbms.2010.6042611
30 https://doi.org/10.1109/cbms.2011.5999110
31 https://doi.org/10.1109/icics.2009.5397624
32 https://doi.org/10.1109/icpr.1994.576366
33 https://doi.org/10.1109/jbhi.2015.2508938
34 https://doi.org/10.1109/jbhi.2016.2526603
35 https://doi.org/10.1109/tbme.2016.2624823
36 https://doi.org/10.1109/tcyb.2017.2685625
37 https://doi.org/10.1109/tip.2010.2044957
38 https://doi.org/10.1109/tkde.2012.242
39 https://doi.org/10.1109/tmi.2013.2268163
40 https://doi.org/10.1109/tmi.2017.2672702
41 https://doi.org/10.1109/tpami.2002.1017623
42 https://doi.org/10.1109/wacv.2013.6475005
43 https://doi.org/10.1136/annrheumdis-2013-203863
44 https://doi.org/10.1137/0106027
45 https://doi.org/10.1504/ijdmb.2017.085713
46 https://doi.org/10.2307/2333955
47 schema:datePublished 2019-03-18
48 schema:datePublishedReg 2019-03-18
49 schema:description The recognition of staining patterns present in human epithelial type 2 (HEp-2) cells helps to diagnose connective tissue disease. In this context, the paper introduces a robust method, termed as CanSuR, for automatic recognition of antinuclear autoantibodies by HEp-2 cell indirect immunofluorescence (IIF) image analysis. The proposed method combines the advantages of a new sequential supervised canonical correlation analysis (CCA), introduced in this paper, with the theory of rough hypercuboid approach. While the proposed CCA efficiently combines the local textural information of HEp-2 cells, derived from various scales of rotation-invariant local binary patterns, the relevant and significant features of HEp-2 cell for staining pattern recognition are extracted using rough hypercuboid approach. Finally, the support vector machine, with radial basis function kernel, is used to recognize one of the known staining patterns present in IIF images. The effectiveness of the proposed staining pattern recognition method, along with a comparison with related approaches, is demonstrated on MIVIA, SNP and ICPR HEp-2 cell image databases. An important finding is that the proposed method performs significantly better than state-of-the art methods, on three HEp-2 cell image databases with respect to both classification accuracy and F1 score.
50 schema:genre research_article
51 schema:inLanguage en
52 schema:isAccessibleForFree false
53 schema:isPartOf sg:journal.1104357
54 schema:name CanSuR: a robust method for staining pattern recognition of HEp-2 cell IIF images
55 schema:pagination 1-19
56 schema:productId N782e58744fc14b1ea1a77aa8de343cc1
57 Nac2fd96023924050b350e7da65c5c91a
58 Nd3011b82288242bc8f36241b0c1c960a
59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112853830
60 https://doi.org/10.1007/s00521-019-04108-w
61 schema:sdDatePublished 2019-04-11T12:13
62 schema:sdLicense https://scigraph.springernature.com/explorer/license/
63 schema:sdPublisher Nd48780c4e9c84ddeac8fffdb4cba3cf8
64 schema:url https://link.springer.com/10.1007%2Fs00521-019-04108-w
65 sgo:license sg:explorer/license/
66 sgo:sdDataset articles
67 rdf:type schema:ScholarlyArticle
68 N28059386248149bc9cb0e1bf503747b0 schema:affiliation https://www.grid.ac/institutes/grid.39953.35
69 schema:familyName Maji
70 schema:givenName Pradipta
71 rdf:type schema:Person
72 N2d8a0a91e202419ca90c723a708be0b4 rdf:first N7ff127e25ab84aab9fa3780d570a581d
73 rdf:rest N3be9699c4d4d46e3946ce65bd482360e
74 N3be9699c4d4d46e3946ce65bd482360e rdf:first N28059386248149bc9cb0e1bf503747b0
75 rdf:rest rdf:nil
76 N782e58744fc14b1ea1a77aa8de343cc1 schema:name doi
77 schema:value 10.1007/s00521-019-04108-w
78 rdf:type schema:PropertyValue
79 N7ff127e25ab84aab9fa3780d570a581d schema:affiliation https://www.grid.ac/institutes/grid.39953.35
80 schema:familyName Mandal
81 schema:givenName Ankita
82 rdf:type schema:Person
83 Nac2fd96023924050b350e7da65c5c91a schema:name dimensions_id
84 schema:value pub.1112853830
85 rdf:type schema:PropertyValue
86 Nd3011b82288242bc8f36241b0c1c960a schema:name readcube_id
87 schema:value 7374b2d4edaeb532ae05d07df208ca3d4b19eb0f88da207c85788af85c390cf2
88 rdf:type schema:PropertyValue
89 Nd48780c4e9c84ddeac8fffdb4cba3cf8 schema:name Springer Nature - SN SciGraph project
90 rdf:type schema:Organization
91 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
92 schema:name Information and Computing Sciences
93 rdf:type schema:DefinedTerm
94 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
95 schema:name Artificial Intelligence and Image Processing
96 rdf:type schema:DefinedTerm
97 sg:journal.1104357 schema:issn 0941-0643
98 1433-3058
99 schema:name Neural Computing and Applications
100 rdf:type schema:Periodical
101 sg:pub.10.1007/978-1-4757-2440-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027312764
102 https://doi.org/10.1007/978-1-4757-2440-0
103 rdf:type schema:CreativeWork
104 sg:pub.10.1007/978-3-642-25346-1_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032660039
105 https://doi.org/10.1007/978-3-642-25346-1_8
106 rdf:type schema:CreativeWork
107 sg:pub.10.1186/1471-2105-12-483 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001580986
108 https://doi.org/10.1186/1471-2105-12-483
109 rdf:type schema:CreativeWork
110 https://app.dimensions.ai/details/publication/pub.1075817644 schema:CreativeWork
111 https://doi.org/10.1002/art.10561 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008555084
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1002/art.30084 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049116187
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1002/cyto.a.20224 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052578672
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1016/0304-4076(76)90010-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030912135
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1016/j.asoc.2016.03.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026707550
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/j.autrev.2009.02.033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025285970
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/j.csda.2013.09.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024905613
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/j.ins.2017.08.083 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091396825
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/j.jaut.2009.08.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040755997
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1016/j.neucom.2005.12.126 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038265102
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/j.patcog.2013.09.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045132756
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/j.patcog.2013.09.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047370072
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/j.patcog.2013.09.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038289541
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/j.patrec.2016.02.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041045850
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1016/s0031-3203(00)00010-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039662290
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1016/s0065-2776(08)60641-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015609776
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1080/03009740500202664 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018572099
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1088/0266-5611/11/3/007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024596623
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1109/bibe.2012.6399750 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093520640
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1109/cbms.2006.21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093334414
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1109/cbms.2006.53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093351997
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1109/cbms.2010.6042611 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093774591
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1109/cbms.2011.5999110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094877441
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1109/icics.2009.5397624 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095348187
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1109/icpr.1994.576366 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094758643
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1109/jbhi.2015.2508938 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061277204
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1109/jbhi.2016.2526603 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061277224
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1109/tbme.2016.2624823 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061530339
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1109/tcyb.2017.2685625 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084605586
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1109/tip.2010.2044957 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061642447
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1109/tkde.2012.242 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061662608
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1109/tmi.2013.2268163 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061696130
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1109/tmi.2017.2672702 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083937086
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1109/tpami.2002.1017623 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061742396
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1109/wacv.2013.6475005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093431445
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1136/annrheumdis-2013-203863 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053373773
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1137/0106027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062837648
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1504/ijdmb.2017.085713 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091134660
186 rdf:type schema:CreativeWork
187 https://doi.org/10.2307/2333955 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069895403
188 rdf:type schema:CreativeWork
189 https://www.grid.ac/institutes/grid.39953.35 schema:alternateName Indian Statistical Institute
190 schema:name Biomedical Imaging and Bioinformatics Lab, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India
191 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...