Breast cancer diagnosis through active learning in content-based image retrieval

One of the cornerstones of content-based image retrieval (CBIR) for medical image diagnosis is to select the images that present higher similarity with a given query image. Different from previous literature efforts, the present work aims to seamlessly fuse a powerful machine learning strategy based...

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Published inNeurocomputing (Amsterdam) Vol. 357; pp. 1 - 10
Main Authors Bressan, Rafael S., Bugatti, Pedro H., Saito, Priscila T.M.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 10.09.2019
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Abstract One of the cornerstones of content-based image retrieval (CBIR) for medical image diagnosis is to select the images that present higher similarity with a given query image. Different from previous literature efforts, the present work aims to seamlessly fuse a powerful machine learning strategy based on the active learning paradigm, in order to obtain greater efficacy regarding similarity queries in medical CBIR systems. To do so, we propose a new approach, named as Medical Active leaRning and Retrieval (MARRow) to aid the breast cancer diagnosis. It enables to deal with more feasible strategies, specifically for the medical context and its inherent constraints. We also proposed an active learning strategy to select a small set of more informative images, considering selection criteria based on not only similarity, but also on certain degrees of diversity and uncertainty. To validate our proposed approach, we performed experiments using public medical image datasets, different descriptors for each one and compared our approach against four widely applied and well-known literature approaches, such as: Traditional CBIR without relevance feedback strategies, Query Point Movement Strategy (QPM), Query Expansion (QEX) and SVM Active Learning (SVM-AL). From the experiments, we can observe that our approach presents a strong performance over state-of-the-art ones reaching a precision gain of up to 87.3%. MARRow also presented a well-suited and consistent increasing rate along the learning iterations. Moreover, our approach can significantly minimize the expert’s involvement in the analysis and annotation process (reducing up to 88%). The results testify that MARRow improves the precision of the similarity queries. It is capable to explore at the maximum the experts’ intentions, which are captured during the relevance feedback process, incrementally improving the learning model. Therefore, our approach can be suitable and applied in challenging processes, such as real and medical contexts, enhancing medical decision support systems (e.g. breast cancer diagnosis).
AbstractList One of the cornerstones of content-based image retrieval (CBIR) for medical image diagnosis is to select the images that present higher similarity with a given query image. Different from previous literature efforts, the present work aims to seamlessly fuse a powerful machine learning strategy based on the active learning paradigm, in order to obtain greater efficacy regarding similarity queries in medical CBIR systems. To do so, we propose a new approach, named as Medical Active leaRning and Retrieval (MARRow) to aid the breast cancer diagnosis. It enables to deal with more feasible strategies, specifically for the medical context and its inherent constraints. We also proposed an active learning strategy to select a small set of more informative images, considering selection criteria based on not only similarity, but also on certain degrees of diversity and uncertainty. To validate our proposed approach, we performed experiments using public medical image datasets, different descriptors for each one and compared our approach against four widely applied and well-known literature approaches, such as: Traditional CBIR without relevance feedback strategies, Query Point Movement Strategy (QPM), Query Expansion (QEX) and SVM Active Learning (SVM-AL). From the experiments, we can observe that our approach presents a strong performance over state-of-the-art ones reaching a precision gain of up to 87.3%. MARRow also presented a well-suited and consistent increasing rate along the learning iterations. Moreover, our approach can significantly minimize the expert’s involvement in the analysis and annotation process (reducing up to 88%). The results testify that MARRow improves the precision of the similarity queries. It is capable to explore at the maximum the experts’ intentions, which are captured during the relevance feedback process, incrementally improving the learning model. Therefore, our approach can be suitable and applied in challenging processes, such as real and medical contexts, enhancing medical decision support systems (e.g. breast cancer diagnosis).
Author Bugatti, Pedro H.
Saito, Priscila T.M.
Bressan, Rafael S.
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Cites_doi 10.1016/j.compbiomed.2014.10.006
10.1145/2071389.2071390
10.1109/TMM.2018.2838320
10.1016/j.compbiomed.2017.11.014
10.1371/journal.pcbi.1006278
10.1109/TSMC.1973.4309314
10.1109/34.55109
10.1016/j.patcog.2009.08.017
10.1371/journal.pcbi.1006058
10.1109/TGRS.1990.572934
10.1016/j.compbiomed.2018.08.006
10.1016/j.neucom.2014.06.027
10.1109/TKDE.2008.188
10.1145/1899412.1899414
10.1016/j.ijmedinf.2011.08.001
10.1007/s007990050026
10.1016/j.neucom.2013.08.007
10.1016/j.compbiomed.2018.03.003
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Keywords Computer vision
Image analysis
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Breast cancer diagnosis
Machine learning
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References Samet (bib0021) 2006
Feng, Liu, Xiao, Hong, Wu (bib0013) 2015; 147
Wang, Hua (bib0019) 2011; 2
Yan, Li, Zhang, Liu, Zhang, Dai (bib0001) 2019; PP
Miranda, Felipe (bib0009) 2015; 64
Kihm, Kaestner, Wagner, Quint (bib0008) 2018; 14
Guo, Zhang, Zhang (bib0026) 2010; 43
Baeza-Yates, Ribeiro-Neto (bib0032) 2011
Yan, Tu, Wang, Zhang, Hao, Zhang, Dai (bib0002) 2019
Turki, Wei (bib0004) 2018; 101
Malode, Gumaste (bib0011) 2015; 20
Hoi, Lyu (bib0030) 2005; 2
Uluwitige, Geva, Zuccon, Chandran, Chappell (bib0012) 2016
He, Wang (bib0027) 1990; 28
Haralick, Shanmugam, Dinstein (bib0025) 1973; SMC-3
Stehling, Nascimento, Falcão (bib0022) 2002
Liu, Zeng, Gong, Yang, Zhai, Cao, Liu, Luo, Li, Maguire, Ding (bib0006) 2018; 92
Won, Park, Park (bib0023) 2002; 24
Carpineto, Romano (bib0015) 2012; 44
Liu, Hua, Vu, Yu (bib0016) 2009; 21
Tenório, Hummel, Cohrs, Sdepanian, Pisa, de Fátima Marin (bib0010) 2011; 80
Wang, Chan, Zhang (bib0031) 2003; 1
Fondón, Sarmiento, Garca, Silvestre, Eloy, Polnia, Aguiar (bib0005) 2018; 96
Nixon, Aguado (bib0024) 2012
Khotanzad, Hong (bib0029) 1990; 12
Yan, Xie, Chen, Zha, Hao, Zhang, Dai (bib0003) 2018; 20
Wang, Li, Yang, Chen (bib0014) 2014; 127
Chen, Scholz, Zhou, Lange (bib0007) 2018; 14
Settles (bib0017) 2009
Oliveira, Scabora, Cazzolato, Bedo, Traina, Traina-Jr. (bib0020) 2017
Wang, Wiederhold, Firschein, Wei (bib0028) 1998; 1
Kremer, Steenstrup Pedersen, Igel (bib0018) 2014; 4
Liu (10.1016/j.neucom.2019.05.041_bib0006) 2018; 92
He (10.1016/j.neucom.2019.05.041_bib0027) 1990; 28
Haralick (10.1016/j.neucom.2019.05.041_bib0025) 1973; SMC-3
Wang (10.1016/j.neucom.2019.05.041_bib0014) 2014; 127
Khotanzad (10.1016/j.neucom.2019.05.041_bib0029) 1990; 12
Turki (10.1016/j.neucom.2019.05.041_bib0004) 2018; 101
Carpineto (10.1016/j.neucom.2019.05.041_bib0015) 2012; 44
Tenório (10.1016/j.neucom.2019.05.041_bib0010) 2011; 80
Wang (10.1016/j.neucom.2019.05.041_bib0028) 1998; 1
Miranda (10.1016/j.neucom.2019.05.041_bib0009) 2015; 64
Uluwitige (10.1016/j.neucom.2019.05.041_bib0012) 2016
Settles (10.1016/j.neucom.2019.05.041_bib0017) 2009
Kihm (10.1016/j.neucom.2019.05.041_bib0008) 2018; 14
Hoi (10.1016/j.neucom.2019.05.041_bib0030) 2005; 2
Kremer (10.1016/j.neucom.2019.05.041_bib0018) 2014; 4
Fondón (10.1016/j.neucom.2019.05.041_bib0005) 2018; 96
Chen (10.1016/j.neucom.2019.05.041_bib0007) 2018; 14
Baeza-Yates (10.1016/j.neucom.2019.05.041_bib0032) 2011
Yan (10.1016/j.neucom.2019.05.041_bib0003) 2018; 20
Liu (10.1016/j.neucom.2019.05.041_bib0016) 2009; 21
Won (10.1016/j.neucom.2019.05.041_bib0023) 2002; 24
Oliveira (10.1016/j.neucom.2019.05.041_bib0020) 2017
Stehling (10.1016/j.neucom.2019.05.041_bib0022) 2002
Samet (10.1016/j.neucom.2019.05.041_bib0021) 2006
Wang (10.1016/j.neucom.2019.05.041_bib0019) 2011; 2
Yan (10.1016/j.neucom.2019.05.041_bib0002) 2019
Malode (10.1016/j.neucom.2019.05.041_bib0011) 2015; 20
Guo (10.1016/j.neucom.2019.05.041_bib0026) 2010; 43
Wang (10.1016/j.neucom.2019.05.041_sbref0031) 2003; 1
Nixon (10.1016/j.neucom.2019.05.041_bib0024) 2012
Yan (10.1016/j.neucom.2019.05.041_bib0001) 2019; PP
Feng (10.1016/j.neucom.2019.05.041_bib0013) 2015; 147
References_xml – volume: 20
  start-page: 3389
  year: 2018
  end-page: 3398
  ident: bib0003
  article-title: A fast uyghur text detector for complex background images
  publication-title: IEEE Trans. Multimed.
  contributor:
    fullname: Dai
– volume: 28
  start-page: 509
  year: 1990
  end-page: 512
  ident: bib0027
  article-title: Texture unit, texture spectrum, and texture analysis
  publication-title: IEEE Trans. Geosci. Remote Sensing
  contributor:
    fullname: Wang
– volume: 21
  start-page: 729
  year: 2009
  end-page: 743
  ident: bib0016
  article-title: Fast query point movement techniques for large cbir systems
  publication-title: IEEE Trans. Knowl. Data Eng.
  contributor:
    fullname: Yu
– start-page: 256
  year: 2017
  end-page: 266
  ident: bib0020
  article-title: MAMMOSET: An Enhanced Dataset of Mammograms
  publication-title: Satellite Events of the Brazilian Symposium on Databases
  contributor:
    fullname: Traina-Jr.
– volume: 44
  start-page: 1:1
  year: 2012
  end-page: 1:50
  ident: bib0015
  article-title: A survey of automatic query expansion in information retrieval
  publication-title: ACM Comput. Surv.
  contributor:
    fullname: Romano
– volume: SMC-3
  start-page: 610
  year: 1973
  end-page: 621
  ident: bib0025
  article-title: Textural features for image classification
  publication-title: IEEE Trans. Syst. Man Cybern.
  contributor:
    fullname: Dinstein
– year: 2012
  ident: bib0024
  article-title: Feature Extraction & Image Processing for Computer Vision
  contributor:
    fullname: Aguado
– year: 2011
  ident: bib0032
  article-title: Modern Information Retrieval: The Concepts and Technology Behind Search
  contributor:
    fullname: Ribeiro-Neto
– volume: 12
  start-page: 489
  year: 1990
  end-page: 497
  ident: bib0029
  article-title: Invariant image recognition by zernike moments
  publication-title: IEEE Trans. Pattern Anal. Mach.Intell.
  contributor:
    fullname: Hong
– start-page: 102
  year: 2002
  end-page: 109
  ident: bib0022
  article-title: A compact and efficient image retrieval approach based on border/interior pixel classification
  publication-title: Intl. Conf. on Information and Knowledge Management
  contributor:
    fullname: Falcão
– volume: 147
  start-page: 509
  year: 2015
  end-page: 522
  ident: bib0013
  article-title: A novel CBIR system with WLLTSA and ULRGA
  publication-title: Neurocomputing
  contributor:
    fullname: Wu
– volume: 14
  start-page: 1
  year: 2018
  end-page: 10
  ident: bib0007
  article-title: Lailaps-qsm: a restful api and java library for semantic query suggestions
  publication-title: PLOS Comput. Biol.
  contributor:
    fullname: Lange
– year: 2009
  ident: bib0017
  article-title: Active Learning Literature Survey
  publication-title: Technical Report
  contributor:
    fullname: Settles
– volume: 92
  start-page: 168
  year: 2018
  end-page: 175
  ident: bib0006
  article-title: Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach
  publication-title: Comput. Biol. Med.
  contributor:
    fullname: Ding
– volume: PP
  start-page: 1
  year: 2019
  end-page: 10
  ident: bib0001
  article-title: Cross-modality bridging and knowledge transferring for image understanding
  publication-title: IEEE Trans. Multimed.
  contributor:
    fullname: Dai
– volume: 20
  start-page: 883
  year: 2015
  end-page: 885
  ident: bib0011
  article-title: A review paper on content based image retrieval
  publication-title: Intl. Res. J. Eng. Technol.
  contributor:
    fullname: Gumaste
– volume: 80
  start-page: 793
  year: 2011
  end-page: 802
  ident: bib0010
  article-title: Artificial intelligence techniques applied to the development of a decision support system for diagnosing celiac disease
  publication-title: Int. J. Med. Inf.
  contributor:
    fullname: de Fátima Marin
– volume: 2
  start-page: 302
  year: 2005
  end-page: 309
  ident: bib0030
  article-title: A semi-supervised active learning framework for image retrieval
  publication-title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  contributor:
    fullname: Lyu
– volume: 64
  start-page: 334
  year: 2015
  end-page: 346
  ident: bib0009
  article-title: Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization
  publication-title: Comput. Biol. Med.
  contributor:
    fullname: Felipe
– volume: 4
  start-page: 313
  year: 2014
  end-page: 326
  ident: bib0018
  article-title: Active Learning With Support Vector Machines
  contributor:
    fullname: Igel
– volume: 1
  year: 2003
  ident: bib0031
  article-title: Bootstrapping svm active learning by incorporating unlabelled images for image retrieval
  publication-title: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition
  contributor:
    fullname: Zhang
– year: 2006
  ident: bib0021
  article-title: Foundations of Multidimensional and Metric Data Structures
  contributor:
    fullname: Samet
– volume: 24
  start-page: 23
  year: 2002
  end-page: 30
  ident: bib0023
  article-title: Efficient use of mpeg-7 edge histogram descriptor
  publication-title: Electron. Telecommun. Res.Instit. J.
  contributor:
    fullname: Park
– volume: 96
  start-page: 41
  year: 2018
  end-page: 51
  ident: bib0005
  article-title: Automatic classification of tissue malignancy for breast carcinoma diagnosis
  publication-title: Comput. Biol. Med.
  contributor:
    fullname: Aguiar
– volume: 43
  start-page: 706
  year: 2010
  end-page: 719
  ident: bib0026
  article-title: Rotation invariant texture classification using LBP variance (LBPV) with global matching
  publication-title: Pattern Recognit.
  contributor:
    fullname: Zhang
– volume: 2
  start-page: 10:1
  year: 2011
  end-page: 10:21
  ident: bib0019
  article-title: Active learning in multimedia annotation and retrieval: A survey
  publication-title: ACM Trans. Intell. Syst. Technol.
  contributor:
    fullname: Hua
– volume: 127
  start-page: 214
  year: 2014
  end-page: 230
  ident: bib0014
  article-title: An image retrieval scheme with relevance feedback using feature reconstruction and svm reclassification
  publication-title: Neurocomputing
  contributor:
    fullname: Chen
– volume: 101
  start-page: 236
  year: 2018
  end-page: 249
  ident: bib0004
  article-title: Boosting support vector machines for cancer discrimination tasks
  publication-title: Comput. Biol. Med.
  contributor:
    fullname: Wei
– year: 2019
  ident: bib0002
  article-title: Stat: Spatial-temporal attention mechanism for video captioning
  publication-title: IEEE Trans. Multimed.
  contributor:
    fullname: Dai
– volume: 14
  start-page: 1
  year: 2018
  end-page: 15
  ident: bib0008
  article-title: Classification of red blood cell shapes in flow using outlier tolerant machine learning
  publication-title: PLOS Comput. Biol.
  contributor:
    fullname: Quint
– volume: 1
  start-page: 311
  year: 1998
  end-page: 328
  ident: bib0028
  article-title: Content-based image indexing and searching using daubechies’ wavelets
  publication-title: Int. J. Digit. Libr.
  contributor:
    fullname: Wei
– start-page: 49
  year: 2016
  end-page: 56
  ident: bib0012
  article-title: Effective user relevance feedback for image retrieval with image signatures
  publication-title: Australasian Document Computing Symposium
  contributor:
    fullname: Chappell
– volume: 64
  start-page: 334
  year: 2015
  ident: 10.1016/j.neucom.2019.05.041_bib0009
  article-title: Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization
  publication-title: Comput. Biol. Med.
  doi: 10.1016/j.compbiomed.2014.10.006
  contributor:
    fullname: Miranda
– year: 2019
  ident: 10.1016/j.neucom.2019.05.041_bib0002
  article-title: Stat: Spatial-temporal attention mechanism for video captioning
  publication-title: IEEE Trans. Multimed.
  contributor:
    fullname: Yan
– volume: 44
  start-page: 1:1
  issue: 1
  year: 2012
  ident: 10.1016/j.neucom.2019.05.041_bib0015
  article-title: A survey of automatic query expansion in information retrieval
  publication-title: ACM Comput. Surv.
  doi: 10.1145/2071389.2071390
  contributor:
    fullname: Carpineto
– volume: 2
  start-page: 302
  year: 2005
  ident: 10.1016/j.neucom.2019.05.041_bib0030
  article-title: A semi-supervised active learning framework for image retrieval
  contributor:
    fullname: Hoi
– year: 2011
  ident: 10.1016/j.neucom.2019.05.041_bib0032
  contributor:
    fullname: Baeza-Yates
– volume: 20
  start-page: 3389
  issue: 12
  year: 2018
  ident: 10.1016/j.neucom.2019.05.041_bib0003
  article-title: A fast uyghur text detector for complex background images
  publication-title: IEEE Trans. Multimed.
  doi: 10.1109/TMM.2018.2838320
  contributor:
    fullname: Yan
– volume: 92
  start-page: 168
  year: 2018
  ident: 10.1016/j.neucom.2019.05.041_bib0006
  article-title: Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach
  publication-title: Comput. Biol. Med.
  doi: 10.1016/j.compbiomed.2017.11.014
  contributor:
    fullname: Liu
– volume: 14
  start-page: 1
  issue: 6
  year: 2018
  ident: 10.1016/j.neucom.2019.05.041_bib0008
  article-title: Classification of red blood cell shapes in flow using outlier tolerant machine learning
  publication-title: PLOS Comput. Biol.
  doi: 10.1371/journal.pcbi.1006278
  contributor:
    fullname: Kihm
– volume: SMC-3
  start-page: 610
  issue: 6
  year: 1973
  ident: 10.1016/j.neucom.2019.05.041_bib0025
  article-title: Textural features for image classification
  publication-title: IEEE Trans. Syst. Man Cybern.
  doi: 10.1109/TSMC.1973.4309314
  contributor:
    fullname: Haralick
– volume: 12
  start-page: 489
  issue: 5
  year: 1990
  ident: 10.1016/j.neucom.2019.05.041_bib0029
  article-title: Invariant image recognition by zernike moments
  publication-title: IEEE Trans. Pattern Anal. Mach.Intell.
  doi: 10.1109/34.55109
  contributor:
    fullname: Khotanzad
– volume: 43
  start-page: 706
  issue: 3
  year: 2010
  ident: 10.1016/j.neucom.2019.05.041_bib0026
  article-title: Rotation invariant texture classification using LBP variance (LBPV) with global matching
  publication-title: Pattern Recognit.
  doi: 10.1016/j.patcog.2009.08.017
  contributor:
    fullname: Guo
– volume: 14
  start-page: 1
  issue: 3
  year: 2018
  ident: 10.1016/j.neucom.2019.05.041_bib0007
  article-title: Lailaps-qsm: a restful api and java library for semantic query suggestions
  publication-title: PLOS Comput. Biol.
  doi: 10.1371/journal.pcbi.1006058
  contributor:
    fullname: Chen
– volume: 28
  start-page: 509
  issue: 4
  year: 1990
  ident: 10.1016/j.neucom.2019.05.041_bib0027
  article-title: Texture unit, texture spectrum, and texture analysis
  publication-title: IEEE Trans. Geosci. Remote Sensing
  doi: 10.1109/TGRS.1990.572934
  contributor:
    fullname: He
– volume: 24
  start-page: 23
  issue: 1
  year: 2002
  ident: 10.1016/j.neucom.2019.05.041_bib0023
  article-title: Efficient use of mpeg-7 edge histogram descriptor
  publication-title: Electron. Telecommun. Res.Instit. J.
  contributor:
    fullname: Won
– volume: PP
  start-page: 1
  year: 2019
  ident: 10.1016/j.neucom.2019.05.041_bib0001
  article-title: Cross-modality bridging and knowledge transferring for image understanding
  publication-title: IEEE Trans. Multimed.
  contributor:
    fullname: Yan
– year: 2009
  ident: 10.1016/j.neucom.2019.05.041_bib0017
  article-title: Active Learning Literature Survey
  contributor:
    fullname: Settles
– start-page: 256
  year: 2017
  ident: 10.1016/j.neucom.2019.05.041_bib0020
  article-title: MAMMOSET: An Enhanced Dataset of Mammograms
  contributor:
    fullname: Oliveira
– volume: 101
  start-page: 236
  year: 2018
  ident: 10.1016/j.neucom.2019.05.041_bib0004
  article-title: Boosting support vector machines for cancer discrimination tasks
  publication-title: Comput. Biol. Med.
  doi: 10.1016/j.compbiomed.2018.08.006
  contributor:
    fullname: Turki
– volume: 147
  start-page: 509
  year: 2015
  ident: 10.1016/j.neucom.2019.05.041_bib0013
  article-title: A novel CBIR system with WLLTSA and ULRGA
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2014.06.027
  contributor:
    fullname: Feng
– volume: 21
  start-page: 729
  issue: 5
  year: 2009
  ident: 10.1016/j.neucom.2019.05.041_bib0016
  article-title: Fast query point movement techniques for large cbir systems
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2008.188
  contributor:
    fullname: Liu
– volume: 2
  start-page: 10:1
  issue: 2
  year: 2011
  ident: 10.1016/j.neucom.2019.05.041_bib0019
  article-title: Active learning in multimedia annotation and retrieval: A survey
  publication-title: ACM Trans. Intell. Syst. Technol.
  doi: 10.1145/1899412.1899414
  contributor:
    fullname: Wang
– volume: 1
  year: 2003
  ident: 10.1016/j.neucom.2019.05.041_sbref0031
  article-title: Bootstrapping svm active learning by incorporating unlabelled images for image retrieval
  contributor:
    fullname: Wang
– volume: 20
  start-page: 883
  year: 2015
  ident: 10.1016/j.neucom.2019.05.041_bib0011
  article-title: A review paper on content based image retrieval
  publication-title: Intl. Res. J. Eng. Technol.
  contributor:
    fullname: Malode
– start-page: 49
  year: 2016
  ident: 10.1016/j.neucom.2019.05.041_bib0012
  article-title: Effective user relevance feedback for image retrieval with image signatures
  contributor:
    fullname: Uluwitige
– year: 2012
  ident: 10.1016/j.neucom.2019.05.041_bib0024
  contributor:
    fullname: Nixon
– volume: 80
  start-page: 793
  issue: 11
  year: 2011
  ident: 10.1016/j.neucom.2019.05.041_bib0010
  article-title: Artificial intelligence techniques applied to the development of a decision support system for diagnosing celiac disease
  publication-title: Int. J. Med. Inf.
  doi: 10.1016/j.ijmedinf.2011.08.001
  contributor:
    fullname: Tenório
– volume: 1
  start-page: 311
  issue: 4
  year: 1998
  ident: 10.1016/j.neucom.2019.05.041_bib0028
  article-title: Content-based image indexing and searching using daubechies’ wavelets
  publication-title: Int. J. Digit. Libr.
  doi: 10.1007/s007990050026
  contributor:
    fullname: Wang
– year: 2006
  ident: 10.1016/j.neucom.2019.05.041_bib0021
  contributor:
    fullname: Samet
– volume: 127
  start-page: 214
  year: 2014
  ident: 10.1016/j.neucom.2019.05.041_bib0014
  article-title: An image retrieval scheme with relevance feedback using feature reconstruction and svm reclassification
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2013.08.007
  contributor:
    fullname: Wang
– volume: 96
  start-page: 41
  year: 2018
  ident: 10.1016/j.neucom.2019.05.041_bib0005
  article-title: Automatic classification of tissue malignancy for breast carcinoma diagnosis
  publication-title: Comput. Biol. Med.
  doi: 10.1016/j.compbiomed.2018.03.003
  contributor:
    fullname: Fondón
– volume: 4
  start-page: 313
  year: 2014
  ident: 10.1016/j.neucom.2019.05.041_bib0018
  contributor:
    fullname: Kremer
– start-page: 102
  year: 2002
  ident: 10.1016/j.neucom.2019.05.041_bib0022
  article-title: A compact and efficient image retrieval approach based on border/interior pixel classification
  contributor:
    fullname: Stehling
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Snippet One of the cornerstones of content-based image retrieval (CBIR) for medical image diagnosis is to select the images that present higher similarity with a given...
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SubjectTerms Active learning
Breast cancer diagnosis
Computer vision
Image analysis
Image retrieval
Machine learning
Title Breast cancer diagnosis through active learning in content-based image retrieval
URI https://dx.doi.org/10.1016/j.neucom.2019.05.041
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