Publication:


abstract


Related Articles
Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions.
Phys Med Biol. 2007
Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions.
Carrara M, Bono A, Bartoli C, Colombo A, Lualdi M, Moglia D, Santoro N, Tolomio E, Tomatis S, Tragni G, et al. Phys Med Biol. 2007 May 7; 52(9):2599-613. Epub 2007 Apr 17.
Pre-diagnostic digital imaging prediction model to discriminate between malignant melanoma and benign pigmented skin lesion.
Skin Res Technol. 2010
Pre-diagnostic digital imaging prediction model to discriminate between malignant melanoma and benign pigmented skin lesion.
Christensen JH, Soerensen MB, Linghui Z, Chen S, Jensen MO. Skin Res Technol. 2010 Feb; 16(1):98-108.
The impact of total body photography on biopsy rate in patients from a pigmented lesion clinic.
J Am Acad Dermatol. 2007
The impact of total body photography on biopsy rate in patients from a pigmented lesion clinic.
Risser J, Pressley Z, Veledar E, Washington C, Chen SC. J Am Acad Dermatol. 2007 Sep; 57(3):428-34. Epub 2007 Jul 10.
Review Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions.
Br J Dermatol. 2004
Review Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions.
Blum A, Luedtke H, Ellwanger U, Schwabe R, Rassner G, Garbe C. Br J Dermatol. 2004 Nov; 151(5):1029-38.
Review Computer-aided melanoma diagnosis.
Dermatol Clin. 2002
Review Computer-aided melanoma diagnosis.
Elbaum M. Dermatol Clin. 2002 Oct; 20(4):735-47, x-xi.