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Fine-Grained Classification of Unpigmented Skin Cancer from Paired Dermatoscopy Images

Gummeson, Anna LU ; Flood, Gabrielle LU orcid ; Nielsen, Kari LU orcid ; Nätterdahl, Carolina LU orcid ; Johansson, Fredrik LU orcid ; Ingvar, Åsa LU orcid and Arvidsson, Ida LU orcid (2025) 23rd Scandinavian Conference on Image Analysis, SCIA 2025 In Lecture Notes in Computer Science 15726 LNCS. p.293-306
Abstract

Unpigmented skin cancer is the most prevalent form of cancer, and it burdens healthcare substantially even if it is not as aggressive as the more well-known malignant melanoma. Dermatoscopy images are commonly used for diagnosis, but differentiating between the many sub-diagnoses is a hard task. In this study we focus on these unpigmented cancers, performing both detection of basal cell carcinoma as well as fine-grained classification of its subclasses. We do this using a new dataset with more than 2’000 cases from a fair-skinned population. A deep learning model is specially designed for the task, handling pairs of polarised and non-polarised dermatoscopy images as input. We investigate transfer learning with different backbones as... (More)

Unpigmented skin cancer is the most prevalent form of cancer, and it burdens healthcare substantially even if it is not as aggressive as the more well-known malignant melanoma. Dermatoscopy images are commonly used for diagnosis, but differentiating between the many sub-diagnoses is a hard task. In this study we focus on these unpigmented cancers, performing both detection of basal cell carcinoma as well as fine-grained classification of its subclasses. We do this using a new dataset with more than 2’000 cases from a fair-skinned population. A deep learning model is specially designed for the task, handling pairs of polarised and non-polarised dermatoscopy images as input. We investigate transfer learning with different backbones as well as adding a mid-step of contrastive learning. The performance is compared to the accuracy of dermatologists on the subset of our test data where we have additional ground truth from histopathological diagnosis. This is the first study focusing on fine-grained classification of unpigmented lesions using only dermatoscopic images, and we reach a balanced accuracy of 39.4%, to be compared to 51.9% for dermatologists. This is a promising first step towards an algorithm to assist dermatologists in their work and we hope that this will open up for further studies on this interesting problem.

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Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Deep learning, Dermatoscopy, Fine-grained classification, Unpigmented skin cancer
host publication
Image Analysis - 23rd Scandinavian Conference, SCIA 2025, Proceedings
series title
Lecture Notes in Computer Science
editor
Petersen, Jens and Dahl, Vedrana Andersen
volume
15726 LNCS
pages
293 - 306
publisher
Springer Science and Business Media B.V.
conference name
23rd Scandinavian Conference on Image Analysis, SCIA 2025
conference location
Reykjavik, Iceland
conference dates
2025-06-23 - 2025-06-25
external identifiers
  • scopus:105009823663
ISSN
1611-3349
0302-9743
ISBN
9783031959172
DOI
10.1007/978-3-031-95918-9_21
language
English
LU publication?
yes
additional info
Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
id
b642a2de-8ef8-4e55-8b8d-a661da1b0c88
date added to LUP
2025-09-11 22:20:02
date last changed
2025-09-26 03:27:54
@inproceedings{b642a2de-8ef8-4e55-8b8d-a661da1b0c88,
  abstract     = {{<p>Unpigmented skin cancer is the most prevalent form of cancer, and it burdens healthcare substantially even if it is not as aggressive as the more well-known malignant melanoma. Dermatoscopy images are commonly used for diagnosis, but differentiating between the many sub-diagnoses is a hard task. In this study we focus on these unpigmented cancers, performing both detection of basal cell carcinoma as well as fine-grained classification of its subclasses. We do this using a new dataset with more than 2’000 cases from a fair-skinned population. A deep learning model is specially designed for the task, handling pairs of polarised and non-polarised dermatoscopy images as input. We investigate transfer learning with different backbones as well as adding a mid-step of contrastive learning. The performance is compared to the accuracy of dermatologists on the subset of our test data where we have additional ground truth from histopathological diagnosis. This is the first study focusing on fine-grained classification of unpigmented lesions using only dermatoscopic images, and we reach a balanced accuracy of 39.4%, to be compared to 51.9% for dermatologists. This is a promising first step towards an algorithm to assist dermatologists in their work and we hope that this will open up for further studies on this interesting problem.</p>}},
  author       = {{Gummeson, Anna and Flood, Gabrielle and Nielsen, Kari and Nätterdahl, Carolina and Johansson, Fredrik and Ingvar, Åsa and Arvidsson, Ida}},
  booktitle    = {{Image Analysis - 23rd Scandinavian Conference, SCIA 2025, Proceedings}},
  editor       = {{Petersen, Jens and Dahl, Vedrana Andersen}},
  isbn         = {{9783031959172}},
  issn         = {{1611-3349}},
  keywords     = {{Deep learning; Dermatoscopy; Fine-grained classification; Unpigmented skin cancer}},
  language     = {{eng}},
  pages        = {{293--306}},
  publisher    = {{Springer Science and Business Media B.V.}},
  series       = {{Lecture Notes in Computer Science}},
  title        = {{Fine-Grained Classification of Unpigmented Skin Cancer from Paired Dermatoscopy Images}},
  url          = {{http://dx.doi.org/10.1007/978-3-031-95918-9_21}},
  doi          = {{10.1007/978-3-031-95918-9_21}},
  volume       = {{15726 LNCS}},
  year         = {{2025}},
}