M. A. Anastasio, H. Yoshida, R. Nagel, R. M. Nishikawa, and K. Doi, "A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms," Med. Phys., vol. 25, no. 9, p. 1613, 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... binary [7 9,70,85,88, 125,138,153] 2, 22, 25, 33, 72, 73, 85, 86, 98,99,110,165, 166] 5, 20, 24, 29, 31, 42, 53, 58, 60, 83, 108, 115, 154, 167, 174, 176,179,197] 187,189 191] Genetic Multidimensional [28, 30,55] 23,136,192] algorithms Real valued [54] [3,100,156] [38, 57,87] Rule encoding [11,12] 12] Indexed [84] Genetic programming [11, 52,114] Evolution strategies [7 9] 135] Evolutionary programming [46,114,188] Hybrid Evolutionary fuzzy [7 9, 70,125] 86] 176] systems Evolutionary neural [7 9, 46, 122, 188] 22, ....

....depression after mania, prevision of tractolimus blood level in liver tansplantation patients, and patient s survival estimation in different types of cancer (malignant skin melanoma, lung cancer, colorectal cancer, and gestational trophoblastic tumours) 2. Medical imaging and signal processing [3, 5, 20, 24, 28 31, 42, 53, 55, 58, 60, 83, 84, 100, 108,115,154,156,167,174,176,179,197]. The fields of medical imaging and signal processing have developed tools to deal with huge amounts of data expressed as images or other types of temporal signals. All but one of the papers in this category deal with problems related to clinical tests, including: thorax radiography, retinal and ....

[Article contains additional citation context not shown here]

M. A. Anastasio, H. Yoshida, R. Nagel, R. M. Nishikawa, and K. Doi. A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms. Medical Physics, 25(9):1613--20, September 1998.

.... signal processing Planning and scheduling Prognosis Diagnosis [5,15,18,21,23,27,37,41,43,52,69,72,84,91, 98,101 103] 1,16,19,24,48,49,54,55, Unidimensional, binary [7 9,47,54,57,75,79,83] 93,94,96,105] 60,61,70,89,90] 20,22,39] Multidimensional [17,77,104] 38] Real valued [2,62,85] [25,40,56] Rule encoding [10,11] 11] 53] Indexed [10,36,71] Genetic programming Evolution strategies [7 9] 76] Evolutionary programming [30,71,100] 12] Classifier systems [12,74,80] Hybrid systems [94] 55] 7 9,47,75] Evolutionary fuzzy [16,19,24,48,49,60,70,90] ....

....skin melanoma, lung cancer, colorectal cancer, and gestational trophoblastic tumours) 4.1.2. Medical imaging and signal processing The fields of medical imaging and signal processing have developed tools to deal with huge amounts of data expressed as images or other types of temporal signals [2,5,15,18,20 23,27,37,39,41,43,52,53,62,69,72,84,85,91,93,94,96,105]. All but one of the papers in this category deal with problems related to clinical tests, including: thorax radiography, retinal and cardiac angiography, computarized tomography, magnetoencephalography, ultrasound imaging, electroencephalography, electrocardiography, radiographic cephalography, ....

[Article contains additional citation context not shown here]

Anastasio MA, Yoshida H, Nagel R, Nishikawa RM, Doi K. A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms. Med Phys 1998;25(9):1613 -- 20.

....detections. GAs are currently applied to many diverse and di#cult optimization problems (see [2] and [3] In a number of applications where the search space was too large for other heuristic methods or too complex for analytic treatment GAs produced favorable results. Other researchers in [4] and [5] have shown that GAs could improve the performance of a CAD scheme. In the present study, we will evaluate how the parameter values fluctuations influence the performance of the CAD scheme and which parameters more a#ect the cost function; our goal is as well to select, by using a GA, the ....

Anastasio, M., Yoshida, H., Nagel, R., Nishikawa, R. M., Doi, K.: A Genetic Algorithm-Based Method for Optimizing the Performance of a Computer-Aided Diagnosis Scheme for Detection of Clustered Microcalcifications in Mammograms. Med. Phys. 25 (1998) 1559--1566

.... binary [7 9, 47, 54, 57, 75, 79, 83] 1,16,19,24,48, 49,54,55,60,61, 70, 89, 90] 5, 15, 18, 21, 23, 27, 37, 41, 43, 52, 69, 72, 84, 91, 93, 94, 96,105] 98, 101 103] Genetic Multidimensional [20, 22, 39] 17, 77,104] algorithms Real valued [38] [2, 62, 85] [25, 40, 56] Rule encoding [10, 11] 11] Indexed [53] Genetic programming [10, 36, 71] Evolution strategies [7 9] 76] Evolutionary programming [30, 71,100] Classifier systems [12, 74, 80] 12] Hybrid Evolutionary fuzzy [7 9, 47, 75] 55] 94] systems ....

.... after mania, prevision of tractolimus blood level in liver tansplantation patients, and patient s survival estimation in different types of cancer (malignant skin melanoma, lung cancer, colorectal cancer, and gestational trophoblastic tumours) ii) Medical imaging and signal processing [2, 5,15,18, 20 23,27,37,39,41,43, 52, 53, 62, 69, 72, 84, 85, 91, 93, 94, 96, 105]. The fields of medical imaging and signal processing have developed tools to deal with huge amounts of data expressed as images or other types of temporal signals. All but one of the papers in this category deal with problems related to clinical tests, including: thorax radiography, retinal and ....

[Article contains additional citation context not shown here]

M. A. Anastasio, H. Yoshida, R. Nagel, R. M. Nishikawa, and K. Doi. A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms. Medical Physics, 25(9):1613--20, September 1998.

....and Simulations, 882] Magn. Reson. Med. 1130] Magn. Reson. Med. USA) 1130] Magnetic Resonance Imaging, 499] MATCH Communications in Mathematical and in Computer Chemistry, 347] Mater. Sci. Forum, 844] Meas Control, 228] Measurement Science Technology, 465] Med. Phys. USA) [862] Medical Engineering and Physics, 147] Medical Physics, 137, 139, 151] Medical Physics (Woodbury) 152] Microchem. J. 380] Microw. Opt. Technol. Lett. USA) 528] Microwave and Optical Technology Letters, 529] Microwave Opt. Tech. Lett. 498] Mitsui Zosen Tech. Rev. Japan) 214] ....

....Kurt, 357] Allred, Lloyd G. 683] Alonso, C. 362] Alphones, A. 464] Altman, Z. 743] Altman, Zwi A. 720] Altman, Zwi, 730, 744] Altona, Cornelis, 597] Altshuler, Edward E. 714, 731, 745, 746, 533] Altshuler, Edward F. 487] Alvers, M. 886] Amir, A. 1054] Anastasio, M. A. [862] Anbarasu, L. A. 575] Anderson, A. P. 706, 708, 719, 740, 526] Andrade Filho, Antonio Carlos Bittencourt de, 316] Andre, David, 1041] Andreu, J. M. 1036] Androulakis, I. P. 96] Ankenbrandt, Carol Ann, 561] Anon, 642] Anon. 1138, 1055] Antel, J. 324] Ara, K. 473, 539] ....

[Article contains additional citation context not shown here]

M. A. Anastasio, H. Yoshida, R. Nagel, R. M. Nishikawa, and K. Doi. A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalci cations in mammograms. Med. Phys. (USA), 25(9):1613-1620, 1998. yCCA82151/98 ga98aAnastasi.

....improve both the sensitivity and specificity. Traditional methods of classifier training attempt to combine these two objective functions (or two analogous class performance measures) into a single scalar objective function that permits the use of conventional (scalar) optimization techniques [8]. A drawback to this approach is that the proper way of aggregating the objective functions is usually unknown. There are, in fact, an infinite number of ways of mapping two objective functions to a single scalar function. Even when a priori information about the relative importance of the two ....

....information about the relative importance of the two objective functions is available, it is not always clear how to incorporate it in the aggregating approach to objective function design. Sometimes, numerous ad hoc combination functions are tried until a suitable objective function is found [8]. Most classifiers do not aggregate sensitivity and specificity directly. Artificial neural networks, for example, typically employ a sum of squares error function [5] which can still be thought of as a sum of two noncommensurable objectives, i.e. one objective is to map abnormal observations to ....

[Article contains additional citation context not shown here]

M. A. Anastasio, H. Yoshida, R. Nagel, R. M. Nishikawa, and K. Doi, "A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms," Med. Phys., vol. 25, no. 9, p. 1613, 1998.

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