Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues.

Journal: Journal of biomedical optics
PMID: 33247560

Abstract

SIGNIFICANCE: Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transparency to be widely adopted as stand-alone diagnostics.

Authors

  • Daniel S Gareau
    The Rockefeller University, Laboratory of Investigative Dermatology, New York, New York, United States.
  • James Browning
    The Rockefeller University, Laboratory of Investigative Dermatology, New York, New York, United States.
  • Joel Correa Da Rosa
    The Rockefeller University, Laboratory of Investigative Dermatology, New York, New York, United States.
  • Mayte Suarez-Farinas
    Icahn School of Medicine at Mount Sinai Medical Center, Department of Dermatology, New York, New Yor, United States.
  • Samantha Lish
    The Rockefeller University, Laboratory of Investigative Dermatology, New York, New York, United States.
  • Amanda M Zong
    The Rockefeller University, Laboratory of Investigative Dermatology, New York, New York, United States.
  • Benjamin Firester
    The Rockefeller University, Laboratory of Investigative Dermatology, New York, New York, United States.
  • Charles Vrattos
    The Rockefeller University, Laboratory of Investigative Dermatology, New York, New York, United States.
  • Yael Renert-Yuval
    The Rockefeller University, Laboratory of Investigative Dermatology, New York, New York, United States.
  • Mauricio Gamboa
    Hospital Clínic de Barcelona, Universitat de Barcelona, Department of Dermatology, Barcelona, Spain.
  • María G Vallone
    Hospital Alemán, Department of Dermatology, Buenos Aires, Argentina.
  • Zamira F Barragán-Estudillo
    Universidad Nacional Autónoma de México, Dermato-Oncology Clinic, Research Division, Faculty of Medi, Mexico.
  • Alejandra L Tamez-Peña
    Hospital Clínic de Barcelona, Universitat de Barcelona, Department of Dermatology, Barcelona, Spain.
  • Javier Montoya
    Universidad San Sebastian, School of Medicine, Concepción, Chile.
  • Miriam A Jesús-Silva
    Hospital Clínic de Barcelona, Universitat de Barcelona, Department of Dermatology, Barcelona, Spain.
  • Cristina Carrera
    Hospital Clínic de Barcelona, Universitat de Barcelona, Department of Dermatology, Barcelona, Spain.
  • Josep Malvehy
    Melanoma Unit, Dermatology Department, Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Rarasd (CIBER ER), Instituto de Salud Carlos III, Barcelona, Spain.
  • Susana Puig
    Melanoma Unit, Dermatology Department, Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Rarasd (CIBER ER), Instituto de Salud Carlos III, Barcelona, Spain.
  • Ashfaq Marghoob
    Dermatology Service, Memorial Sloan Kettering Cancer Center, Hauppauge, New York.
  • John A Carucci
    New York University, Ronald O. Pearlman Department of Dermatology, New York, New York, United States.
  • James G Krueger
    The Rockefeller University, Laboratory of Investigative Dermatology, New York, New York, United States.

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