Facilitating clinically relevant skin tumor diagnostics with spectroscopy-driven machine learning
(2024) In iScience 27(5).- Abstract
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/073753ab-9338-4cb0-bae2-4cdf4148d66f
- author
- organization
-
- Centre for Environmental and Climate Science (CEC)
- Computational Science for Health and Environment (research group)
- Ophthalmology Imaging Research Group (research group)
- Ophthalmology, Lund
- LU Profile Area: Light and Materials
- LUCC: Lund University Cancer Centre
- LUSCaR- Lund University Skin Cancer Research group (research group)
- Dermatology and Venereology (Lund)
- publishing date
- 2024-05-17
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Hyperspectral imaging, Machine learning, Skin tumours
- in
- iScience
- volume
- 27
- issue
- 5
- article number
- 109653
- pages
- 17 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85190775127
- ISSN
- 2589-0042
- DOI
- 10.1016/j.isci.2024.109653
- project
- Computational Science for Health and Environment
- language
- English
- LU publication?
- yes
- id
- 073753ab-9338-4cb0-bae2-4cdf4148d66f
- date added to LUP
- 2024-04-24 09:22:24
- date last changed
- 2024-06-21 04:00:54
@article{073753ab-9338-4cb0-bae2-4cdf4148d66f, abstract = {{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.}}, author = {{Andersson, Emil and Hult, Jenny and Troein, Carl and Stridh, Magne and Sjögren, Benjamin and Pekar-Lukacs, Agnes and Hernandez-Palacios, Julio and Edén, Patrik and Persson, Bertil and Olariu Annell, Victor and Malmsjö, Malin and Merdasa, Aboma}}, issn = {{2589-0042}}, keywords = {{Hyperspectral imaging; Machine learning; Skin tumours}}, language = {{eng}}, month = {{05}}, number = {{5}}, publisher = {{Elsevier}}, series = {{iScience}}, title = {{Facilitating clinically relevant skin tumor diagnostics with spectroscopy-driven machine learning}}, url = {{http://dx.doi.org/10.1016/j.isci.2024.109653}}, doi = {{10.1016/j.isci.2024.109653}}, volume = {{27}}, year = {{2024}}, }