auto SMIM: Automatic Superpixel-Based Masked Image Modeling for Skin Lesion Segmentation
Abstract
Skin lesion segmentation from dermoscopic images plays a vital role in early diagnoses and prognoses of various skin diseases. However, it is a challenging task due to the large variability of skin lesions and their blurry boundaries. Moreover, most existing skin lesion datasets are designed for disease classification, with relatively fewer segmentation labels having been provided. To address these issues, we propose a novel automatic superpixel-based masked image modeling method, named autoSMIM, in a self-supervised setting for skin lesion segmentation. It explores implicit image features from abundant unlabeled dermoscopic images. autoSMIM begins with restoring an input image with randomly masked superpixels. The policy of generating and masking superpixels is then updated via a novel proxy task through Bayesian Optimization. The optimal policy is subsequently used for training a new masked image modeling model. Finally, we finetune such a model on the downstream skin lesion segmentation task. Extensive experiments are conducted on three skin lesion segmentation datasets, including ISIC 2016, ISIC 2017, and ISIC 2018. Ablation studies demonstrate the effectiveness of superpixel-based masked image modeling and establish the adaptability of autoSMIM. Comparisons with state-of-the-art methods show the superiority of our proposed autoSMIM. The source code is available at https://github.com/Wzhjerry/autoSMIM.
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PanDerm achieved significantly higher performance on skin lesion segmentation using ISIC2018 and HAM10000, surpassing next best by 3.1% and 1.9% in Jaccard index (P < 0.001).
“Compared with existing methods including SL-Imagenet, autoSMIM33 and BATFormer33, PanDerm achieved significantly higher performance, surpassing the next best by 3.1% and 1.9% in the Jaccard index on both datasets (P < 0.001; Extended Data Fig. 5a,b).”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [33]
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A Multimodal Vision Foundation Model for Clinical Dermatology
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Self-citation
- Authors
- Wang, Z., Lyu, J., & Tang, X.
- Affiliations
- Southern University of Science and Technology (CN)
- The University of Queensland (AU)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2023-12
- Volume
- 42
- Issue
- 12
- Pages
- 3501-3511
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W4382372249
- Cited by
- 35
- References
- 53
- Citations by year
- 2026: 5 · 2025: 20 · 2024: 9 · 2023: 1
- FWCI
- 4.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Shenzhen Basic Research Program (JCYJ20190809120205578, JCYJ20200925153847004)
- National Natural Science Foundation of China (62071210)
- Shenzhen Science and Technology Program (RCYX20210609103056042)
- Shenzhen Science and Technology Innovation Committee (KCXFZ2020122117340001)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- skin lesion segmentation, dermoscopic images, masked image modeling, superpixels, self-supervised learning, Bayesian optimization, proxy tasks, unlabeled images, dermatological diagnosis
- MeSH terms
- Algorithms, Bayes Theorem, Humans, Image Processing, Computer-Assisted, Skin Diseases, Skin Neoplasms, Dermoscopy
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