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Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Introduction

Breast cancer is globally a major threat for women’s health. Screening and adequate follow-up can significantly reduce the mortality from breast cancer. Human second reading of screening mammograms can increase breast cancer detection rates, whereas this has not been proven for current computer-aided detection systems as “second reader”. Critical factors include the detection accuracy of the systems and the screening experience and training of the radiologist with the system. When assessing the performance of systems and system components, the choice of evaluation methods is particularly critical. Core assets herein are reference image databases and statistical methods.

Methods

We have analyzed characteristics and usage of the currently largest publicly available mammography database, the Digital Database for Screening Mammography (DDSM) from the University of South Florida, in literature indexed in Medline, IEEE Xplore, SpringerLink, and SPIE, with respect to type of computer-aided diagnosis (CAD) (detection, CADe, or diagnostics, CADx), selection of database subsets, choice of evaluation method, and quality of descriptions.

Results

59 publications presenting 106 evaluation studies met our selection criteria. In 54 studies (50.9%), the selection of test items (cases, images, regions of interest) extracted from the DDSM was not reproducible. Only 2 CADx studies, not any CADe studies, used the entire DDSM. The number of test items varies from 100 to 6000. Different statistical evaluation methods are chosen. Most common are train/test (34.9% of the studies), leave-one-out (23.6%), and N-fold cross-validation (18.9%). Database-related terminology tends to be imprecise or ambiguous, especially regarding the term “case”.

Discussion

Overall, both the use of the DDSM as data source for evaluation of mammography CAD systems, and the application of statistical evaluation methods were found highly diverse. Results reported from different studies are therefore hardly comparable. Drawbacks of the DDSM (e.g. varying quality of lesion annotations) may contribute to the reasons. But larger bias seems to be caused by authors’ own decisions upon study design.

Recommendations/conclusion

For future evaluation studies, we derive a set of 13 recommendations concerning the construction and usage of a test database, as well as the application of statistical evaluation methods.

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References

  1. World Health Organisation: (2008) World Health Statistics. WHO Press, Geneva

    Google Scholar 

  2. Levi F, Lucchini F, Negri E, Vecchia CL (2007) Continuing declines in cancer mortality in the European union. Ann Oncol 18(3): 593–595

    Article  PubMed  CAS  Google Scholar 

  3. Gøtzsche PC, Nielsen M (2009) Screening for breast cancer with mammography. Cochrane Database Syst Rev 5034(4): CD001877

    Google Scholar 

  4. Thurfjell EL, Lernevall KA, Taube AA (1994) Benefit of independent double reading in a population-based mammography screening program. Radiology 191(1): 241–244

    PubMed  CAS  Google Scholar 

  5. Warren RM, Duffy SW (1995) Comparison of single reading with double reading of mammograms, and change in effectiveness with experience. Br J Radiol 68(813): 958–962

    Article  PubMed  CAS  Google Scholar 

  6. Taylor P, Potts HWW (2008) Computer aids and human second reading as interventions in screening mammography: two systematic reviews to compare effects on cancer detection and recall rate. Eur J Cancer 44(6): 798–807

    Article  PubMed  Google Scholar 

  7. Gromet M (2008) Comparison of computer-aided detection to double reading of screening mammograms: review of 231,221 mammograms. AJR Am J Roentgenol 190(4): 854–859

    Article  PubMed  Google Scholar 

  8. Fenton JJ, Taplin SH, Carney PA, Abraham L, Sickles EA, D’Orsi C et al (2007) Influence of computer-aided detection on performance of screening mammography. N Engl J Med 356(14): 1399–1409

    Article  PubMed  CAS  Google Scholar 

  9. Gur D, Sumkin JH, Rockette HE, Ganott M, Hakim C, Hardesty L et al (2004) Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. J Natl Cancer Inst 96(3): 185–190

    Article  PubMed  Google Scholar 

  10. Feig SA, Sickles EA, Evans WP, Linver MN (2004) Re: changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. J Natl Cancer Inst 96(16): 1260–1261

    Article  PubMed  Google Scholar 

  11. Bennett RL, Blanks RG, Moss SM (2006) Does the accuracy of single reading with CAD (computer-aided detection) compare with that of double reading?: a review of the literature. Clin Radiol 61(12): 1023–1028

    Article  PubMed  CAS  Google Scholar 

  12. Gilbert FJ, Astley SM, McGee MA, Gillan MGC, Boggis CRM, Griffiths PM et al (2006) Single reading with computer-aided detection and double reading of screening mammograms in the United Kingdom national breast screening program. Radiology 241(1): 47–53

    Article  PubMed  Google Scholar 

  13. Armato SG III, McLennan G, McNitt-Gray MF et al (2004) Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232: 739–748

    Article  PubMed  Google Scholar 

  14. Garcia-Orellana CJ, Gallardo-Caballero R, González-Velasco HM, Garcia-Manso A, Macias-Macias M (2008) Study of a mammographic CAD performance dependence on the considered mammogram set. Conf Proc IEEE Eng Med Biol Soc 2008: 4776–4779

    PubMed  Google Scholar 

  15. Nishikawa RM (2007) Current status and future directions of computer-aided diagnosis in mammography. Comput Med Imaging Graph 31(4–5): 224–235

    Article  PubMed  Google Scholar 

  16. Wirth M, Lyon J, Fraschini M, Nikitenko D (2004) The effect of mammogram databases on algorithm performance. In: Proceedings 17th IEEE symposium on computer-based medical systems CBMS, pp 15–20

  17. FDA (2004) Innovation or stagnation—challenge and opportunity on the critical path to new medical products. FDA

  18. Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer P (2000) The digital database for screening mammography. In: Proceedings 5th International workshop on digital mammography (IWDM), Toronto, pp 212–218

  19. Suckling J, Parker J, Dance DR, Astley SM, Hutt I, Boggis CRM et al (1994) The mammographic image analysis society digital mammogram database. In: International workshop on digital mammography, pp 211–221

  20. Veldkamp WJ, Karssemeijer N (2000) Normalization of local contrast in mammograms. IEEE Trans Med Imaging 19(7): 731–738

    Article  PubMed  CAS  Google Scholar 

  21. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, New York

    Book  Google Scholar 

  22. Boulesteix AL, Strobl C, Augustin T, Daumer M (2008) Evaluating microarray-based classifiers: an overview. Cancer Inf 6: 77–97

    Google Scholar 

  23. Efron B, Gong G (1983) A leisurely look at the bootstrap, the jackknife, and cross-validation. J Am Stat 37(1): 36–48

    Article  Google Scholar 

  24. Efron B, Tibshirani R (1997) Improvements on cross-validation: the. 632+ Bootstrap method. J Am Stat 92(438): 548–560

    Article  Google Scholar 

  25. McLachlan GJ, Chevelu J, Zhu J (2008) Correcting for selection bias via cross-validation in the classification of microarray data. IMS Collect 1: 364–376

    Article  Google Scholar 

  26. Ambroise C, McLachlan GJ (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. In: Proceedings of the national acad sciences USA 97, pp 6562–6566

  27. Tourassi GD, Sharma AC, Singh S, Saunders RS, Lo JY, Samei E et al (2008) Knowledge transfer across breast cancer screening modalities: a pilot study using an information-theoretic CADe system for mass detection. In: Krupinski EA (ed) Proceedings IWDM 2008, vol 5116 of LNCS, pp 292–298

  28. te Brake GM, Karssemeijer N, Hendriks JH (2000) An automatic method to discriminate malignant masses from normal tissue in digital mammograms. Phys Med Biol 45(10): 2843–2857

    Article  PubMed  CAS  Google Scholar 

  29. Campanini R, Dongiovanni D, Iampieri E, Lanconelli N, Masotti M, Palermo G et al (2004) A novel featureless approach to mass detection in digital mammograms based on support vector machines. Phys Med Biol 49(6): 961–975

    Article  PubMed  Google Scholar 

  30. Catarious DM Jr, Baydush AH, Floyd CE Jr (2006) Characterization of difference of Gaussian filters in the detection of mammographic regions. Med Phys 33(11): 4104–4114

    Article  PubMed  Google Scholar 

  31. Russakoff DB, Hasegawa A (2006) Generation and application of a probabilistic breast cancer atlas. In: Larsen R, Nielsen M, Sporring J (eds) Proceedings MICCAI 2006, vol 4191 of LNCS. Springer, pp 454–461

  32. Vitulano S, Casanova A (2008) The role of entropy: mammogram analysis. In: Campilho A, Kamel M (eds) Proceedings ICIAR 2008, vol 5112 of LNCS. Springer, pp 863–872

  33. Yao B, Jiang J, Peng Y (2004) A CBR driven genetic algorithm for microcalcification cluster detection. In: Motta E et al (eds) Proceedings EKAW 2004, vol 3257 of LNAI. Springer, pp 494– 496

  34. Li Y, Jiang J (2004) Combination of SVM knowledge for microcalcification detection in digital mammograms. In: Yang ZR et al (eds) Proceedings IDEAL 2004, vol 3177 of LNCS. Springer, pp 359–365

  35. Mazurowski MA, Habas PA, Zurada JM, Tourassi GD (2008) Decision optimization of case-based computer-aided decision systems using genetic algorithms with application to mammography. Phys Med Biol 53(4): 895–908

    Article  PubMed  Google Scholar 

  36. Oliver A, Lladó X, Martí J, Martí R, Freixenet J (2007) False positive reduction in breast mass detection using two-dimensional PCA. In: Martí J et al (eds) Proceedings IbPRIA 2007, Part II. vol 4478 of LNCS. Springer, pp 154–161

  37. Pereira RR, Marques PMA, Honda MO, Kinoshita SK, Engelmann R, Muramatsu C et al (2007) Usefulness of texture analysis for computerized classification of breast lesions on mammograms. J Digit Imaging 20(3): 248–255

    Article  PubMed  Google Scholar 

  38. Panchal R, Verma B (2006) Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network. In: King I et al (eds) Proceedings ICONIP 2006, Part III. vol 4234 of LNCS. Springer, pp 127–136

  39. Elter M, Schulz-Wendtland R, Wittenberg T (2007) The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. Med Phys 34(11): 4164–4172

    Article  PubMed  CAS  Google Scholar 

  40. Mazurowski MA, Zurada JM, Tourassi GD (2008) Reliability assessment of ensemble classifiers: application in mammography. In: Krupinski EA (ed) Proceedings IWDM 2008, vol 5116 of LNCS. Springer, pp 366–370

  41. Shi J, Sahiner B, Chan HP, Ge J, Hadjiiski L, Helvie MA et al (2008) Characterization of mammographic masses based on level set segmentation with new image features and patient information. Med Phys 35(1): 280–290

    Article  PubMed  Google Scholar 

  42. Varela C, Timp S, Karssemeijer N (2006) Use of border information in the classification of mammographic masses. Phys Med Biol 51(2): 425–441

    Article  PubMed  CAS  Google Scholar 

  43. Kallergi M, Clark RA, Clarke LP (1997) Medical image databases for CAD applications in digital mammography: design issues. Stud Health Technol Inform 43 Part B: 601–605

    Google Scholar 

  44. Schiabel H, Nunes FL, Escarpinati MC, Benatti RH (2001) Investigations on the effect of different characteristics of images sets on the performance of a processing scheme for microcalcifications detection in digital mammograms. J Digit Imaging 14(2 Suppl 1): 224–225

    Article  PubMed  CAS  Google Scholar 

  45. Perconti P, Loew MH (2006) An objective measure for assembling databases used to train and test mammogram CAD algorithms. In: Proceedings 3rd IEEE international symposium on biomedical imaging: Nano to Macro, pp 1340–1343

  46. Warren R, Solomonides AE, del Frate C, Warsi I, Ding J, Odeh M et al (2007) MammoGrid–a prototype distributed mammographic database for Europe. Clin Radiol 62(11): 1044–1051

    Article  PubMed  CAS  Google Scholar 

  47. Oliveira JEE, Gueld MO, de Araujo AA, Ott B, Deserno TM (2008) Toward a standard reference database for computer-aided mammography. In: Giger ML, Karssemeijer N (eds) Proceedings medical imaging 2008: computer-aided diagnosis, vol 6915 of SPIE. SPIE, pp 69151Y–1–69151Y–9

  48. Tangaro S, Bellotti R, Carlo FD, Gargano G, Lattanzio E, Monno P et al (2008) MAGIC-5: an Italian mammographic database of digitised images for research. Radiol Med 113(4): 477–485

    Article  PubMed  CAS  Google Scholar 

  49. Altrichter M, Ludányi Z, Horváth G (2005) Joint analysis of multiple mammographic views in CAD systems for breast cancer detection. In: Kalviainen H, et al. (eds) Proceedings SCIA 2005, vol 3540 of LNCS. Springer, pp 760–769

  50. Altrichter M, Horváth G (2006) The refinement of microcalcification cluster assessment by joint analysis of MLO and CC views. In: Astley SM, Brady M, Rose C, Zwiggelaar R et al (eds) Proceedings IWDM 2006, vol 4046 of LNCS. Springer, pp 509–516

  51. Baydush AH, Catarious DM, Abbey CK, Floyd CE (2003) Computer aided detection of masses in mammography using subregion hotelling observers. Med Phys 30(7): 1781–1787

    Article  PubMed  Google Scholar 

  52. Baydush AH, Catarious DM, CE Floyd Jr (2003) Computer-aided detection of masses in mammography using a Laguerre-Gauss channelized hotelling observer. In: Chakraborty DP, Krupinski EA (eds) Proceedings medical imaging 2003: image perception, observer performance, and technology assessment, vol 5034 of SPIE. SPIE, pp 71–76

  53. Bedard ND, Sampat MP, Stokes PA, Markey MK (2006) Reducing false-positive detections by combining two stage-1 computer-aided mass detection algorithms. In: Reinhardt JM, Pluim JPW (eds) Proceedings medical imaging 2006: image processing, vol 6144 of SPIE. SPIE, pp 61445U–1–61445U–8

  54. Beller M, Stotzka R, Müller T, Gemmeke H (2005) An example-based system to support the segmentation of stellate lesions. In: Meinzer HP, Handels H, Horsch A, Tolxdorff T (eds) Proceedings Bildverarbeitung für die Medizin 2005. Springer, pp 475–479

  55. Boryczko K, Kurdziel M (2005) Recognition of subte microcalcifications in high-resolution mammograms. In: Kurzynski M, Puchata E, Wozniak M, Zolnierek A (eds) Proceedings CORES 2005, vol 30 of advances in soft computing. Springer, pp 485–492. Zuvor als ”Proc. ICCRS 2007” fehlklassifiziert!

  56. Catarious DM Jr, Baydush AH, Floyd CE Jr (2004) Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system. Med Phys 31(6): 1512–1520

    Article  PubMed  Google Scholar 

  57. Eltonsy NH, Tourassi GD, Habas PA, Elmaghraby AS (2005) DNA: directional neighborhood analysis for detection of breast masses in screening mammograms. In: Fitzpatrick JM, Reinhardt JM (eds) Proceedings medical imaging 2005: image processing, vol 5747 of SPIE. SPIE, pp 38–47

  58. Eltonsy NH, Tourassi GD, Elmaghraby AS (2007) A concentric morphology model for the detection of masses in mammography. IEEE Trans Med Imaging 26(6): 880–889

    Article  PubMed  Google Scholar 

  59. Gallardo-Caballero R, García-Orellana CJ, González-Velasco HM, Macías-Macías M (2007a) Independent component analysis applied to detection of early breast cancer signs. In: Sandoval F et al (eds) Proceedings IWANN 2007, vol 4507 of LNCS. Springer, pp 988–995

  60. García-Orellana CJ, Gallardo-Caballero R, Macías-Macias M, González-Velasco H (2007b) SVM and neural networks comparison in mammographic CAD. Conf Proc IEEE Eng Med Biol Soc 2007: 3204–3207

    PubMed  Google Scholar 

  61. Habas PA, Zurada JM, Elmaghraby AS, Tourassi GD (2006) Probabilistic framework for reliability analysis of information-theoretic CAD systems in mammography. Conf Proc IEEE Eng Med Biol Soc 1: 6113–6116

    Article  PubMed  Google Scholar 

  62. Habas PA, Zurada JM, Tourassi GD (2008) Case-specific reliability assessment for improved false positive reduction with an information-theoretic CAD system. In: Krupinski EA (ed) Proceedings IWDM 2008, vol 5116 of LNCS. Springer, pp 329–335

  63. Kang HK, Kim SM, Thanh NN, Ro YM, Kim WH (2004) Adaptive microcalcification detection in computer aided diagnosis. In: Bubak M et al (eds) Proceedings ICCS 2004, vol 3039 of LNCS. Springer, pp 1110–1117

  64. Kurdziel M, Boryczko K, Yuen DA (2007) Detecting clusters of microcalcifications in high-resolution mammograms using support vector machines. Bio Algorithms Med Syst 3(6): 11–21

    Google Scholar 

  65. Masotti M (2006) A ranklet-based image representation for mass classification in digital mammograms. Med Phys 33(10): 3951–3961

    Article  PubMed  Google Scholar 

  66. de Oliveira ML, da Silva EC, Silva AC, de Paiva AC, Gattass M (2007) Classification of breast tissues in mammogram images using Ripley’s K function and support vector machine. In: Kamel M, Campilho A (eds) Proceedings ICIAR 2007, vol 4633 of LNCS. Springer, pp 899–910

  67. Oliver A, Lladó X, Freixenet J, Martí J (2007) False positive reduction in mammographic mass detection using local binary patterns. In: Ayache N, Ourselin S, Maeder A (eds) Proceedings MICCAI 2007, Part I. vol 4791 of LNCS. Springer, pp 286–293

  68. Regentova E, Zhang L, Zheng J, Veni G (2007) Microcalcification detection based on wavelet domain hidden markov tree model: study for inclusion to computer aided diagnostic prompting system. Med Phys 34(6): 2206–2219

    Article  PubMed  Google Scholar 

  69. Sampat MP, Bovik AC, Whitman GJ, Markey MK (2008) A model-based framework for the detection of spiculated masses on mammography. Med Phys 35(5): 2110–2123

    Article  PubMed  Google Scholar 

  70. Tourassi GD, Vargas-Voracek R, Catarious DM Jr, Floyd CE Jr (2003) Computer-assisted detection of mammographic masses: a template matching scheme based on mutual information. Med Phys 30(8): 2123–2130

    Article  PubMed  Google Scholar 

  71. Tourassi GD, Vargas-Voracek R, Floyd CE Jr (2003) Content-based image retrieval as a computer aid for the detection of mammographic masses. In: Sonka M, Fitzpatrick JM (eds) Proceedings medical imaging 2003: image processing, vol 5032 of SPIE. SPIE, pp 590–597

  72. Tourassi GD, Eltonsy NH, Elmaghraby AS, Floyd CE Jr (2005) Automated detection of mammographic masses: preliminary assessment of an information-theoretic CAD scheme for reduction of false positives. In: Fitzpatrick JM, Reinhardt JM (eds) Proceedings medical imaging 2005: image processing, vol 5747 of SPIE. SPIE, pp 947–954

  73. Tourassi GD, Harrawood B, Singh S, Lo JY, Floyd CE (2007) Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms. Med Phys 34(1): 140–150

    Article  PubMed  Google Scholar 

  74. Tourassi GD, Harrawood B, Floyd CE Jr (2007) Cross-digitizer robustness of a knowledge-based CAD system for mass detection in mammograms. In: Giger ML, Karssemeijer N (eds) Proceedings medical imaging 2007: computer-aided diagnosis, vol 6514 of SPIE. SPIE, pp. 65141Y–1–65141Y–8

  75. Veni G, Regentova EE, Mandava AK (2008) A new method of detecting microcalcification clusters for computer aided digital mammography. In: Proceedings ICSENG 2008, IEEE. IEEE, pp 532–537. Proceedings 19th international conference on systems engineering ICSENG ’08

  76. Vibert JF, Valleron AJ (2003) The retina as a neuromimetic model to extract data in noisy images : application to detection of microcalcification clusters in mammography. In: Proceeedings AMIA 2003 annual Symposium, pp 684–688

  77. Yuan X, Shi P (2004) Microcalcification detection based on localized texture comparison. In: Proceedings ICIP 2004, vol 5, pp 2953–2956. Proceedings international conference on image processing ICIP ’04

  78. Zheng Y, Agyepong K (2007) Mass detection with digitized screening mammograms by using Gabor features. In: Giger ML, Karssemeijer N (eds) Proceedings medical imaging 2007: computer-aided diagnosis, vol 6514 of SPIE. SPIE, pp 651402–1–651402–12

  79. Eltonsy NH, Elmaghraby AS, Tourassi GD (2007) Bilateral breast volume asymmetry in screening mammograms as a potential marker of breast cancer: preliminary experience. In: Proceedings IEEE international conference on image processing ICIP 2007, vol 5 of IEEE. IEEE, pp V–5–V–8

  80. von der Heidt SR, Elter M, Wittenberg T, Paulus D (2009) Model-based characterization of mammographic masses. In: Proceedings bildverarbeitung für die Medizin 2009, pp 287–291

  81. Ike RC III, Singh S, Harrawood B, Tourassi GD (2008) Effect of ROI size on the performance of an information-theoretic CAD system in mammography: multi-size fusion analysis. In: Giger ML, Karssemeijer N (eds) Proceedings medical imaging 2008: computer-aided diagnosis, vol 6915. SPIE, pp 691527–1–691527–7

  82. Karahaliou A, Skiadopoulos S, Boniatis I, Sakellaropoulos P, Likaki E, Panayiotakis G et al (2007) Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis. Br J Radiol 80(956): 648–656

    Article  PubMed  CAS  Google Scholar 

  83. Karahaliou AN, Boniatis IS, Skiadopoulos SG, Sakellaropoulos FN, Arikidis NS, Likaki EA et al (2008) Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications. IEEE Trans Inf Technol Biomed 12(6): 731–778

    Article  PubMed  Google Scholar 

  84. Khuwaja GA, Abu-Rezq AN (2004) Bi-modal breast cancer classification system. Pattern Anal Appl 7: 235–242

    Google Scholar 

  85. Kim S, Yoon S (2007) Mass lesions classification in digital mammography using optimal subset of BI-RADS and gray level features. In: Proceedings ITAB 2007, IEEE. IEEE, pp 99–102. Proceedings 6th international special topic conference on information technology applications in biomedicine ITAB 2007

  86. Land WH, McKee D, Velazquez R, Wong L, Lo JY, Anderson FR (2003) Application of support vector machines to breast cancer screening using mammogram and clinical history data. In: Sonka M, Fitzpatrick JM (eds) Proceedings medical imaging 2003: image processing, vol 5032 of SPIE. SPIE, pp 546–556

  87. Land WH, McKee DW, Anderson FR, Lo JY (2004) Breast cancer classification improvements using a new kernel function with evolutionary-programming-configured support vector machines. In: Fitzpatrick JM, Sonka M (eds) Proceedings medical imaging 2004: image processing, vol 5370 of SPIE. SPIE, pp 880–887

  88. Lim WK, Er MJ (2004) Classification of mammographic masses using generalized dynamic fuzzy neural networks. Med Phys 31(5): 1288–1295

    Article  PubMed  Google Scholar 

  89. Shi J, Sahiner B, Chan HP, Hadjiiski LM, Ge J, Wei J (2008) Breast mass classification on full-field digital mammography and screen-film mammography. In: Krupinski EA (ed) Proceedings IWDM 2008, vol 5116 of LNCS. Springer, pp 371–377

  90. Tourassi GD, Floyd CE Jr (2004) Computer-assisted diagnosis of mammographic masses using an information-theoretic image retrieval scheme with BIRADs-based relevance feedback. In: Fitzpatrick JM, Sonka M (eds) Proceedings medical imaging 2004: image processing, vol 5370 of SPIE. SPIE, pp 810–817

  91. Vargas-Voracek R, Tourassi GD, Floyd CE Jr (2002) Spectral characterization of mammographic tissue for computer aided diagnosis of malignant masses. In: Proceedings EMBS / BMES 2002, vol 2 of IEEE. IEEE, pp 1103–1104. Proceedings second joint [Engineering in medicine and biology 24th annual conference and the annual fall meeting of the biomedical engineering society] EMBS/BMES conference

  92. Yoon S, Kim S (2008) AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM. In: Proceedings IEEE international conference on bioinformatics and biomeidcine workshops BIBMW 2008, IEEE. IEEE, pp 75–82

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  1. Institute for Medical Statistics and Epidemiology, TU München, Ismaninger Str. 22, 81675, München, Germany

    Alexander Horsch & Alexander Hapfelmeier

  2. Fraunhofer Institute for Integrated Circuits (IIS), Am Wolfsmantel 33, 91058, Erlangen, Germany

    Matthias Elter

  3. Department of Computer Science and Department of Clinical Medicine, University of Tromsø, Breivika, 9037, Tromsø, Norway

    Alexander Horsch

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Horsch, A., Hapfelmeier, A. & Elter, M. Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies. Int J CARS 6, 749–767 (2011). https://doi.org/10.1007/s11548-011-0553-9

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