[1] A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Souto, and J. J.
Vidal, “Computer-aided diagnosis: Automatic detection of malignant
masses in digitized mammograms,” Med Phys., vol. 25, pp. 957–64,
Jun. 1998.
[2] J. Tang, R. Rangayyan, J. Xu, I. E. Naqa, and Y. Yang, “Computeraided
detection and diagnosis of breast cancer with mammography:
Recent advances,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 2,
pp. 236–251, Mar. 2009.
[3] R. Rubin, D. Strayer, E. Rubin, and J. McDonald, Rubin’s Pathology:
Clinicopathologic Foundations of Medicine. Baltimore, MD: Lippincott
Williams & Wilkins, 2007.
[4] K. L. Weind, C. F. Maier, B. K. Rutt, and M. Moussa, “Invasive carcinomas
and fibroadenomas of the breast: Comparison of microvessel
distributions–implications for imaging modalities,” Radiology, vol.
208, pp. 477–83, Aug. 1998.
[5] P. H. Bartels, D. Thompson, M. Bibbo, and J. E. Weber, “Bayesian
belief networks in quantitative histopathology,” Anal. Quant. Cytol.
Histol., vol. 14, pp. 459–73, Dec. 1992.
[6] P. W. Hamilton, N. Anderson, P. H. Bartels, and D. Thompson, “Expert
system support using Bayesian belief networks in the diagnosis of fine
needle aspiration biopsy specimens of the breast,” J Clin. Pathol., vol.
47, pp. 329–36, Apr. 1994.
[7] W. C. Allsbrook Jr., K. A. Mangold, M. H. Johnson, R. B. Lane, C.
G. Lane, and J. I. Epstein, “Interobserver reproducibility of Gleason
grading of prostatic carcinoma: General pathologist,” Hum. Pathol.,
vol. 32, pp. 81–8, Jan. 2001.
[8] C. R. King and J. P. Long, “Prostate biopsy grading errors: A sampling
problem?,” Int. J. Cancer, vol. 90, pp. 326–30, Dec. 2000.
[9] B. Djavan, K. Kadesky, B. Klopukh, M. Marberger, and C. G.
Roehrborn, “Gleason scores from prostate biopsies obtained with
18-gauge biopsy needles poorly predict Gleason scores of radical
prostatectomy specimens,” Eur. Urol.., vol. 33, pp. 261–70, 1998.
[10] E. Ruijter, G. van Leenders, G. Miller, F. Debruyne, and C. van de Kaa,
“Errors in histological grading by prostatic needle biopsy specimens:
Frequency and predisposing factors,” J Pathol., vol. 192, pp. 229–33,
Oct. 2000.
[11] L. Egevad, W. C. Allsbrook Jr., and J. I. Epstein, “Current practice of
Gleason grading among genitourinary pathologists,” Hum. Pathol., vol.
36, pp. 5–9, Jan. 2005.
[12] M. Ladekarl, “Objective malignancy grading: A review emphasizing
unbiased stereology applied to breast tumors,” APMIS Suppl., vol. 79,
pp. 1–34, 1998.
[13] M. G. Daniel and J. Luthringer, “Gleason grade migration: changes
in prostate cancer grade in the contemporary era,” Departments of
Pathology and Medicine, Cedars-Sinai, Los Angeles, CA, 2001, vol.
9.3, Reprinted from PCRI Insights.
[14] F. S. F. T. A. Stamey, J. E. McNeal, E. A. Redwine, A. S. Whittemore,
and H.-P. Schmid, “Localized prostate cancer. Relationship of
tumor volume to clinical significance for treatment of prostate cancer,”
Cancer, vol. 71, pp. 933–938, 1993.
[15] M. L. Giger, H. P. Chan, and J. Boone, “Anniversary paper: History
and status of CAD and quantitative image analysis: The role of
medical physics and AAPM,” Med. Phys., vol. 35, pp. 5799–820,
Dec. 2008.
[16] T. M. Haygood, G. J. Whitman, E. N. Atkinson, R. G. Nikolova, S. Y.
Sandoval, and P. J. Dempsey, “Results of a survey on digital screening
mammography: Prevalence, efficiency, and use of ancillary diagnostic
AIDS,” J. Amer. Coll. Radiol., vol. 5, pp. 585–92, Apr. 2008.
[17] R. Schulz-Wendtland, K. P. Hermann, T.Wacker, and W. Bautz, “Current
situation and future perspectives of digital mammography,” Radiologe,
vol. 48, pp. 324–34, Apr. 2008.
[18] A. Azari and S. Nikzad, “Computer-assisted implantology: Historical
background and potential outcomes-A review,” Int. J. Med. Robot., vol.
4, pp. 95–104, Jun. 2008.
[19] F. Sardanelli, G. M. Giuseppetti, G. Canavese, L. Cataliotti, S. Corcione,
E. Cossu, M. Federico, L. Marotti, L. Martincich, P. Panizza, F.
Podo, M. Rosselli Del Turco, C. Zuiani, C. Alfano, M. Bazzocchi, P.
Belli, S. Bianchi, A. Cilotti, M. Calabrese, L. Carbonaro, L. Cortesi,
C. Di Maggio, A. Del Maschio, A. Esseridou, A. Fausto, M. Gennaro,
R. Girometti, R. Ienzi, A. Luini, S. Manoukian, S. Morassutt, D. Morrone,
J. Nori, A. Orlacchio, F. Pane, P. Panzarola, R. Ponzone, G. Simonetti,
P. Torricelli, and G. Valeri, “Indications for breast magnetic
resonance imaging. Consensus document “Attualita in senologia”, Florence
2007,” Radiol. Med., vol. 113, pp. 1085–1095, Dec. 2008.
[20] K. Doi, “Computer-aided diagnosis in medical imaging: Historical review,
current status and future potential,” Comput. Med. Imag. Graph.,
vol. 31, pp. 198–211, Jun.-Jul. 2007.
[21] E. Sakka, A. Prentza, and D. Koutsouris, “Classification algorithms for
microcalcifications in mammograms (Review),” Oncol. Rep., vol. 15,
pp. 1049–55, 2006.
[22] L. R. Zhu, Y. Zhang, X. Y.Wang, and Q. P. Liao, “Assessment of magnetic
resonance imaging in preoperative staging of endometrial carcinoma,”
Beijing Da Xue Xue Bao, vol. 38, pp. 249–51, Jun. 2006.
[23] A. C. Roque and T. C. Andre, “Mammography and computerized decision
systems: a review,” Ann. NY Acad. Sci., vol. 980, pp. 83–94, Dec.
2002.
[24] H. Fox, “Is H&E morphology coming to an end?,” Brit. Med. J., vol.
53, p. 38, 2000.
[25] R. Camp, G. Chung, and D. Rimm, “Automated subcellular localization
and quantification of protein expression in tissue microarrays,” Nature
Med., vol. 8, pp. 1323–1328, 2002.
[26] A. Can, M. Bello, X. Tao, M. Gerdes, A. Sood, M. Montalto, and F.
Ginty, “Techniques for cellular quantitation of cancer biomarkers,” in
Microscopic Image Analysis for Life Science Applications. Norwood,
MA: Artech House, 2008.
[27] F. Ginty, S. Adak, A. Can, M. Gerdes, M. Larsen, H. Cline, R. Filkins,
Z. Pang, Q. Li, and M. Montalto, “The relative distribution of membranous
and cytoplasmic met is a prognostic indicator in stage I and II
colon cancer,” Clin. Cancer Res., vol. 14, p. 3814, 2008.
[28] J. Price, A. Goodacre, K. Hahn, L. Hodgson, E. Hunter, S. Krajewski,
R. Murphy, A. Rabinovich, J. Reed, and S. Heynen, “Advances in
molecular labeling, high throughput imaging and machine intelligence
portend powerful functional cellular biochemistry tools,” J. Cell.
Biochem., vol. 87, pp. 194–210, 2002.
[29] A. Can, M. Bello, H. E. Cline, X. Tao, F. Ginty, A. Sood, M. Gerdes,
and M. Montalto, “Multi-modal imaging of histological tissue sections,”
in 5th IEEE Int. Symp. Biomed. Imag.: From Nano to Macro,
2008, pp. 288–291.
[30] W. Schubert, B. Bonnekoh, A. Pommer, L. Philipsen, R. Bockelmann,
Y. Malykh, H. Gollnick, M. Friedenberger, M. Bode, and A. Dress,
“Analyzing proteome topology and function by automated multidimensional
fluorescence microscopy,” Nature Biotechnol., vol. 24, pp.
1270–1278, 2006.
[31] J. Newberg and R. Murphy, “A framework for the automated analysis
of subcellular patterns in human protein atlas images,” J. Proteome
Res., vol. 7, pp. 2300–2308, 2008.
[32] A. Rabinovich, S. Agarwal, C. Laris, J. Price, and S. Belongie, “Unsupervised
color decomposition of histologically stained tissue samples,”
Adv. Neural Inf. Process. Syst., vol. 16, pp. 667–674.
[33] D. Fernandez, R. Bhargava, S. Hewitt, and I. Levin, “Infrared spectroscopic
imaging for histopathologic recognition,” Nature Biotechnol.,
vol. 23, pp. 469–474, 2005.
[34] E. Blout and M. Fields, “On the infrared spectra of nucleic acids and
certain of their components,” Science, vol. 107, p. 252, 1948.
[35] E. R. Blout and R. C. Mellors, “Infrared spectra of tissues,” Science,
vol. 110, pp. 137–138, 1949.
[36] D. Woernley, “Infrared absorption curves for normal and neoplastic
tissues and related biological substances,” Cancer Res., vol. 12, p. 516,
1952.
[37] W. Hollingworth, L. Medina, R. Lenkinski, D. Shibata, B. Bernal, D.
Zurakowski, B. Comstock, and J. Jarvik, “Interrater reliability in assessing
quality of diagnostic accuracy studies using theQUADAS toola
preliminary assessment,” Acad. Radiol., vol. 13, pp. 803–810, 2006.
[38] F. Howe and K. Opstad, “