Use of Machine Learning Tools for Post-Processing of Digital Dermoscopic Images: a Case Series

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Бібліографічні деталі
Опубліковано в::Przeglad Dermatologiczny vol. 112, no. 1 (2025), p. 59-64
Автор: Maińska, Urszula
Інші автори: Żółkiewicz, Jakub, Sobjanek, Michał, Sławińska, Martyna
Опубліковано:
Termedia Publishing House
Онлайн доступ:Citation/Abstract
Full Text - PDF
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022 |a 2084-9893 
024 7 |a 10.5114/dr.2025.152310  |2 doi 
035 |a 3279483494 
045 2 |b d20250101  |b d20250228 
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100 1 |a Maińska, Urszula 
245 1 |a Use of Machine Learning Tools for Post-Processing of Digital Dermoscopic Images: a Case Series 
260 |b Termedia Publishing House  |c 2025 
513 |a Journal Article 
520 3 |a The use of electronic photographic recording has greatly facilitated the process of photo post-processing. Digital camera-based fixation enables extensive manipulation of captured images, potentially uncovering diagnostically relevant features and improving the visualization of dermoscopic structures.This article aims to illustrate the potential utility of digital image post-processing in selected diagnostic contexts. A series of clinical dermoscopic images are presented, demonstrating pre- and post-processing comparisons, with annotated regions highlighting key diagnostic structures.Digital post-processing may offer diagnostic support in certain cases, particularly when used in conjunction with artificial intelligence and machine learning algorithms, which facilitate analysis with minimal user intervention. However, validation of the diagnostic reliability of post-processed images necessitates multicenter, retrospective comparative studies. 
700 1 |a Żółkiewicz, Jakub 
700 1 |a Sobjanek, Michał 
700 1 |a Sławińska, Martyna 
773 0 |t Przeglad Dermatologiczny  |g vol. 112, no. 1 (2025), p. 59-64 
786 0 |d ProQuest  |t Health & Medical Collection 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3279483494/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3279483494/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch