ACNet: Mask-Aware Attention with Dynamic Context Enhancement for Robust Acne Detection

Min, Kyungseo; Lee, Gunhee; Lee, Seong–Whan

doi:10.1109/smc52423.2021.9659243

Cited by 16 publications

(6 citation statements)

References 21 publications

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“…Rashataprucksa et al [6] employed object detection models, Faster-RCNN [8] and R-FCN [9], to detect acne in facial images, comparing these two models to evaluate their respective acne detection capabilities. Min et al [10] utilized a dual encoder composed of CNN and Transformer to extract features, which were then processed through dynamic context enhancement and mask-aware multiattention for final acne detection. Similarly, Junayed et al [11] approached acne detection through semantic segmentation, employing a dual encoder setup with CNN and Transformer.…”

Section: Acne Detectionmentioning

confidence: 99%

“…Then the training loss for each corresponding GT is calculated using equations ( 8) through (10). L seg is the linear combination of Dice loss and Cross-entropy.…”

Section: Loss Computationmentioning

confidence: 99%

“…Rashataprucksa et al [6] and Hyunh et al [7] utilized object detection models such as faster-rcnn [8] and r-fcn [9] for acne detection. Min et al [10] employed a dual encoder based on CNN and Transformer to detect face acne. Junayed et al [11] also proposed a dual encoder based on CNN and Transformer, but they detected acne through a semantic segmentation approach.…”

Section: Introductionmentioning

confidence: 99%

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Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy-Paste

Kim,

Yoon,

Lee

2024

Preprint

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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.

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Section: Acne Detectionmentioning

confidence: 99%

“…Then the training loss for each corresponding GT is calculated using equations ( 8) through (10). L seg is the linear combination of Dice loss and Cross-entropy.…”

Section: Loss Computationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy-Paste

Kim,

Yoon,

Lee

2024

Preprint

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show abstract

“…Rashataprucksa et al [8] employed object detection models Faster-RCNN [10] and R-FCN [11] to detect acne in facial images, comparing these two models to evaluate their respective acne detection capabilities. Min et al [12] utilized a dual encoder composed of a CNN and Transformer to extract features which were then processed through dynamic context enhancement and mask-aware multi-attention for final acne detection. Similarly, Junayed et al [13] approached acne detection through semantic segmentation, employing a dual encoder setup with a CNN and Transformer.…”

Section: Acne Detectionmentioning

confidence: 99%

“…Rashataprucksa et al [8] and Hyunh et al [9] utilized object detection models such as the faster region-based convolutional neural network (Faster-RCNN) [10] and region-based fully convolutional networks (R-FCN) [11] for acne detection. Min et al [12] employed a dual encoder based on a convolutional neural network (CNN) and Transformer to detect face acne. Junayed et al [13] also proposed a dual encoder based on CNN and Transformer, but they detected acne through a semantic segmentation approach.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy–Paste

Kim,

Yoon,

Lee

2024

Diagnostics

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Facial acne is a prevalent dermatological condition regularly observed in the general population. However, it is important to detect acne early as the condition can worsen if not treated. For this purpose, deep-learning-based methods have been proposed to automate detection, but acquiring acne training data is not easy. Therefore, this study proposes 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 in Dice score over previous methods. The proposed semi-supervised learning approach for facial acne segmentation demonstrated an improvement in performance, offering a novel direction for future acne analysis.

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