Version 1
: Received: 8 April 2024 / Approved: 8 April 2024 / Online: 9 April 2024 (07:17:22 CEST)
How to cite:
Kim, S.; Yoon, H.; Lee, J. Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy-Paste. Preprints2024, 2024040591. https://doi.org/10.20944/preprints202404.0591.v1
Kim, S.; Yoon, H.; Lee, J. Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy-Paste. Preprints 2024, 2024040591. https://doi.org/10.20944/preprints202404.0591.v1
Kim, S.; Yoon, H.; Lee, J. Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy-Paste. Preprints2024, 2024040591. https://doi.org/10.20944/preprints202404.0591.v1
APA Style
Kim, S., Yoon, H., & Lee, J. (2024). Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy-Paste. Preprints. https://doi.org/10.20944/preprints202404.0591.v1
Chicago/Turabian Style
Kim, S., Huisu Yoon and Jongha Lee. 2024 "Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy-Paste" Preprints. https://doi.org/10.20944/preprints202404.0591.v1
Abstract
Facial acne is a prevalent dermatological condition regularly observed in the general population. This study introduces a novel deep learning model for facial acne segmentation utilizing a semi-supervised learning method known as bidirectional copy-paste, which synthesizes images by interchanging foreground and background parts between labeled and unlabeled images during the training phase. To overcome the lower performance observed in the labeled image training part compared to the previous methods, a new framework was devised to directly compute the training loss based on labeled images. The effectiveness of the proposed method was evaluated against previous semi-supervised learning methods using images cropped from facial images at acne sites. The proposed method achieved a Dice score of 0.5205 in experiments utilizing only 3% of labels, marking an improvement of 0.0151 to 0.0473 Dice score over previous methods. The proposed semi-supervised learning approach for facial acne segmentation demonstrates improvement in performance, offering a novel direction for future acne analysis.
Keywords
acne segmentation; semi-supervised learning; bidirectional copy-paste; deep learning; semantic segmentation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
