Abstract
A massive-training artificial neural network (MTANN) has been investigated for reduction of false positives (FPs) in computer-aided detection (CAD) of lesions in medical images. The MTANN is trained with a massive number of subvolumes extracted from input volumes; hence the term “massive training”. A major limitation of this technique is a long training time due to the high input dimensionality. To solve this problem, we incorporated principal-component (PC) analysis for dimension reduction into the MTANN framework, which we call a PC-MTANN. To test the PC-MTANN, we compared it with the original MTANN in FP reduction in CAD of polyps in CT colonography. With the use of the dimension reduction architecture, the time required for training was reduced substantially from 38 to 4 hours, while the original performance was maintained, i.e., a 96% sensitivity at an FP rate of 3.2 and 3.0 per patient by the original MTANN and the PC-MTANN, respectively.
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Suzuki, K., Yoshida, H., Nappi, J., Dachman, A.H.: Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes. Med. Phys. 33, 3814–3824 (2006)
Jerebko, A.K., Malley, J.D., Franaszek, M., Summers, R.M.: Support vector machines committee classification method for computer-aided polyp detection in CT colonography. Academic Radiology 12, 479–486 (2005)
Arimura, H., Katsuragawa, S., Suzuki, K., Li, F., Shiraishi, J., Sone, S., Doi, K.: Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad. Radiol. 11, 617–629 (2004)
Suzuki, K., Armato III, S.G., Li, F., Sone, S., Doi, K.: Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med. Phys. 30, 1602–1617 (2003)
Suzuki, K., Shiraishi, J., Abe, H., MacMahon, H., Doi, K.: False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Acad. Radiol. 12, 191–201 (2005)
Suzuki, K., Yoshida, H., Nappi, J., Armato III, S.G., Dachman, A.H.: Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. Med. Phys. 35, 694–703 (2008)
Suzuki, K., Li, F., Sone, S., Doi, K.: Computer-aided diagnostic 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 (2005)
Suzuki, K., Abe, H., MacMahon, H., Doi, K.: Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans. Med. Imaging 25, 406–416 (2006)
Suzuki, K.: A supervised ‘lesion-enhancement’ filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Phys. Med. Biol. 54, S31–S45 (2009)
Suzuki, K., Horiba, I., Sugie, N., Nanki, M.: Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Trans. Med. Imaging 23, 330–339 (2004)
Suzuki, K., Horiba, I., Sugie, N.: Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1582–1596 (2003)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997)
Yoshida, H., Nappi, J.: Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans. Med. Imaging 20, 1261–1274 (2001)
Metz, C.E.: ROC methodology in radiologic imaging. Investigative Radiology 21, 720–733 (1986)
Egan, J.P., Greenberg, G.Z., Schulman, A.I.: Operating characteristics, signal detectability, and the method of free response. Journal of the Acoustical Society of America 33, 993–1007 (1961)
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Suzuki, K., Xu, J., Zhang, J., Sheu, I. (2010). Principal-Component Massive-Training Machine-Learning Regression for False-Positive Reduction in Computer-Aided Detection of Polyps in CT Colonography. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds) Machine Learning in Medical Imaging. MLMI 2010. Lecture Notes in Computer Science, vol 6357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15948-0_23
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DOI: https://doi.org/10.1007/978-3-642-15948-0_23
Publisher Name: Springer, Berlin, Heidelberg
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