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Professor Malin Malmsjö, MD, PhD. Photo.

Malin Malmsjö

Professor

Professor Malin Malmsjö, MD, PhD. Photo.

Facilitating clinically relevant skin tumor diagnostics with spectroscopy-driven machine learning

Author

  • Emil Andersson
  • Jenny Hult
  • Carl Troein
  • Magne Stridh
  • Benjamin Sjögren
  • Agnes Pekar-Lukacs
  • Julio Hernandez-Palacios
  • Patrik Edén
  • Bertil Persson
  • Victor Olariu Annell
  • Malin Malmsjö
  • Aboma Merdasa
Show all

Summary, in English

In the dawning era of artificial intelligence (AI), health care stands to undergo a significant transformation with the increasing digitalization of patient data. Digital imaging, in particular, will serve as an important platform for AI to aid decision making and diagnostics. A growing number of studies demonstrate the potential of automatic pre-surgical skin tumor delineation, which could have tremendous impact on clinical practice. However, current methods rely on having ground truth images in which tumor borders are already identified, which is not clinically possible. We report a novel approach where hyperspectral images provide spectra from small regions representing healthy tissue and tumor, which are used to generate prediction maps using artificial neural networks (ANNs), after which a segmentation algorithm automatically identifies the tumor borders. This circumvents the need for ground truth images, since an ANN model is trained with data from each individual patient, representing a more clinically relevant approach.

Department/s

  • Centre for Environmental and Climate Science (CEC)
  • Computational Science for Health and Environment
  • Ophthalmology Imaging Research Group
  • Ophthalmology, Lund
  • LU Profile Area: Light and Materials
  • LUCC: Lund University Cancer Centre
  • LUSCaR- Lund University Skin Cancer Research group
  • Dermatology and Venereology (Lund)

Publishing year

2024-05-17

Language

English

Publication/Series

iScience

Volume

27

Issue

5

Document type

Journal article

Publisher

Elsevier

Topic

  • Cancer and Oncology
  • Biophysics

Keywords

  • Hyperspectral imaging
  • Machine learning
  • Skin tumours

Status

Published

Project

  • Computational Science for Health and Environment

Research group

  • Computational Science for Health and Environment
  • Ophthalmology Imaging Research Group
  • LUSCaR- Lund University Skin Cancer Research group

ISBN/ISSN/Other

  • ISSN: 2589-0042