• Position: Assistant Professor with time contract (RTD/a)
  • Email: massimo.salvi@polito.it
  • Phone number: 011-090-4109
  • ORCID Identifier: 0000-0001-7225-7401
  • Publications: Publication list at IRIS@POLITO
  • Curriculum Vitae: Download

  • Biography:

    Massimo Salvi was born in San Benedetto del Tronto, Italy. He received his Master’s Degree in Biomedical Engineering from the Politecnico di Torino, Torino, Italy, in 2014. He worked as a Researcher under grant from March to October 2015 at the Department of Electronics and Telecommunications of the Politecnico di Torino, Italy. In 2015 he began his Ph.D. in Biomedical Engineering at the Politecnico di Torino and obtained his Ph.D. degree in March 2018 with the thesis “Modeling and Interpretation of Physiopathological Processes using Multimodal and Multiscale Medical Imaging”. His research is mainly focused on biomedical signal and image processing. He is actively engaged in the field of artificial intelligence and deep learning. He has experience in the development of hybrid techniques that combine AI-based methods and mathematical-statistical techniques to address complex problems in biomedical research. His research is primarily focused on the development of automated solutions for fluorescence and optical microscopy, with a particular emphasis on the analysis and interpretation of cellular and tissue images.

    Research areas:

  • Synthetic image generation using Generative Adversarial Networks (GANs)
  • Dermatological image analysis
  • Digital pathology image analysis
  • Fluorescence microscopy image analysis

  • Ongoing funded projects:

  • REAP - Revealing drug tolerant persister cells in cancer using contrast enhanced optical coherence and photoacoustic tomography
  • Sistema per la normalizzazione della colorazione di preparati istologici in anatomia patologica
  • Discriminazione e quantificazione dei linfociti infiltranti il tumore nel carcinoma della mammella
  • Generazione di immagini CT sintetiche a partire da immagini cone-beam CT
  • DIPAT3-Care: Digital Pathology Pathways to improve Patient Care

  • Teaching activities:

    Teachings list at DET@POLITO

    Thesis proposals:

    Thesis proposals list at DET@POLITO