Topic:

Dermatology is the branch of medicine dealing with the skin and it is a specialty with both medical and surgical aspects. Dermatological imaging plays a key role in the clinical evaluation of skin lesions. The appearance, morphology, shape, and distribution of lesions are features that allow to recognize the manifestation of cutaneous or even systemic disease. The prediction of skin lesions is a challenging task even for experienced dermatologists due to the occasional low contrast between the surrounding skin and lesions, the visual resemblance between skin lesions, a fuddled lesion border, etc. An automated computer-aided detection system can help clinicians to predict malignant skin lesions at the earliest time.

Recent advances in deep learning have paved the way for the use of convolutional neural networks (CNNs) in this medical branch as well.  Their effectiveness, however, is dependent upon the quality and quantity of data available. This research line is focused on the development of fully automated algorithms for the optimization, classification, and segmentation of skin lesion images.

People:

  • Francesco Branciforti (Ph.D. Student)
  • Kristen M. Meiburger (Assistant Professor with time contract (RTD/a))
  • Massimo Salvi (Research Assistant)
  • Collaborators:

  • Prof. Paola Savoia, Elisa Zavattaro, Federica Veronese - University Hospital Maggiore della Carit√†, Novara

  • Recent publications:

  • The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks
      Federica Veronese, Francesco Branciforti, Elisa Zavattaro,Vanessa Tarantino,Valentina Romano, Kristen M. Meiburger, Massimo Salvi, Silvia Seoni, and Paola Savoia
      Diagnostics
      10.3390/diagnostics11030451
  • Automatic Extraction of Dermatological Parameters from Nevi Using an Inexpensive Smartphone Microscope: A Proof of Concept
      Kristen M. Meiburger, Federica Veronese, Vanessa Tarantino, Massimo Salvi, Matteo Fadda, Silvia Seoni, Elisa Zavattaro, Bruno De Santi, Nicola Michielli, Paola Savoia, Filippo Molinari
      2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
      10.1109/EMBC.2019.8856720