Abstract
Computer-aided diagnosis is a diagnostic procedure in which a radiologist uses the outputs of computer analysis of medical images as a second opinion in the interpretation of medical images, either to help with lesion detection or to help determine if the lesion is benign or malignant. Artificial neural networks (ANNs) are usually employed to formulate the statistical models for computer analysis. Receiver operating characteristic curves are used to evaluate the performance of the ANN alone, as well as the diagnostic performance of radiologists who take into account the ANN output as a second opinion. In this chapter, we use mammograms to illustrate how an ANN model is trained, tested, and evaluated, and how a radiologist should use the ANN output as a second opinion in CAD.
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References
Eadie LH, Taylor P, Gibson AP (2012) A systematic review of computer-aided diagnosis in diagnostic cancer imaging. Eur J Radiol 81:e70–e76
Baker JA, Kornguth PJ, Lo JY et al (1996) Artificial neural network: improving the quality of breast biopsy recommendations. Radiology 198:131–135
Chan H, Sahiner B, Petrick N et al (1997) Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network. Phys Med Biol 42:549–567
Jiang Y, Nishikawa RM, Schmidt RA et al (1999) Improving breast cancer diagnosis with computer-aided diagnosis. Acad Radiol 6:22–33
Huo Z, Giger ML, Vyborny CJ et al (2002) Breast cancer: effectiveness of computer-aided diagnosis—observer study with independent database of mammograms. Radiology 224:560–568
Suzuki K, Li F, Sone S et al (2005) Computer-aided diagnosis scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans Med Imaging 24:1138–1150
Richard HH, Lippman RP (1991) Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Comput 3:461–483
Swingler K (1996) Applying neural networks: a practical guide. Academic, London
Berry MJA, Linoff G (1997) Data mining techniques. Wiley, New York
Rana RS, Jiang Y, Schmidt RA et al (2007) Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms. Acad Radiol 14:363–370
Swets JA (1978) ROC analysis applied to the evaluation of medical imaging techniques. Invest Radiol 14:109–121
Metz CE (1986) ROC methodology in radiologic imaging. Invest Radiol 21:720–733
Swets JA (1986) Forms of empirical ROCs in discrimination and diagnostic tasks: implication for theory and measurement of performance. Psychol Bull 99:181–198
Hanley JA (1988) The robustness of the binormal assumption used in fitting ROC curves. Med Decis Making 8:197–203
Veldkamp WJH, Karssemeijer N (1996) Influence of segmentation on classification of microcalcifications in digital mammography. IEEE engineering in medicine and biology society, bridging disciplines for biomedicine, proceedings of the 18th annual international conference of the IEEE 3, pp 1171–1172
Paquerault S, Yarusso LM, Papaioannou J et al (2004) Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance. Med Phys 31:2648–2657
Shi J, Sahiner B, Chan HP 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
Singh S, Maxwell J, Baker JA et al (2011) Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents. Radiology 258:73–80
Matignon R (2005) Neural network modeling using SAS enterprise miner. AuthorHouse, Bloomington
Way TW, Sahiner B, Hadjiiski LM et al (2010) Effect of finite sample size on feature selection and classification: a simulation study. Med Phys 37(2):907–920
Rojas R, Feldman J (1996) Neural networks: a systematic introduction. Springer, New York
Metz CE, Herman BA, Shen JH (1998) Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously distributed data. Stat Med 17:1033–1053
Salfity MF, Nishikawa RM, Jiang Y et al (2003) The use of priori information in the detection of mammographic microcalcifications to improve their classification. Med Phys 30:823–831
Rojas R (1996) A short proof of the posterior probability classifier neural networks. Neural Comput 8:41–43
Liu B, Jiang Y (2013) A multitarget training method for artificial neural network with application to computer-aided diagnosis. Med Phys 40:011908. doi:10.1118/1.4772021
Rosen PP (2001) Rosen’s breast pathology. Lippincott Williams & Wilkins, Philadelphia
Jiang Y, Nishikawa RM, Wolverton DE et al (1996) Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology 198:671–678
Chan H, Sahiner B, Lam KL et al (1998) Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. Med Phys 25:2007–2019
Huo Z, Giger ML, Vyborny CJ (2001) Computerized analysis of multiple-mammographic views: potential usefulness of special view mammograms in computer-aided diagnosis. IEEE Trans Med Imaging 20:1285–1292
Metz CE, Shen J (1992) Gains in accuracy from replicated reading of diagnostic images: prediction and assessment in terms of ROC analysis. Med Decis Making 12:60–75
Liu B, Metz CE, Jiang Y (2004) An ROC comparison of four methods of combining information from multiple images of the same patient. Med Phys 31:2552–2563
Liu B, Metz CE, Jiang Y (2005) Effect of correlation on combining diagnostic information from two images of the same patient. Med Phys 32:3329–3338
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Liu, B. (2015). Application of Artificial Neural Networks in Computer-Aided Diagnosis. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 1260. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_12
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DOI: https://doi.org/10.1007/978-1-4939-2239-0_12
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