JIANet: Jigsaw-Invariant Self-Supervised Learning of Autoencoder-Based Reconstruction for Melanoma Segmentation

Zhang, Hui; Liu, Jiaxuan; Wu, Tianyue; Chen, Yurong; Liu, Caiping; Wang, Yaonan

doi:10.1109/tim.2022.3218033

Cited by 3 publications

(1 citation statement)

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“…The learning framework combines the strength discrimination pretext task with the predicted image rotation angle pretext task to improve the feature extraction ability and generalization performance of the network model. JIANet 26 combined a jigsaw‐invariant instance discrimination task and an autoencoding reconstruct task to explore image‐level features and pixel‐level features, which acquire identical representations from unlabeled medical images.…”

Section: Related Workmentioning

confidence: 99%

Skin lesion classification based on hybrid self‐supervised pretext task

Yang,

Zhang,

et al. 2024

Int J Imaging Syst Tech

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The combination of observation of skin lesion and digital image technology contributes to the diagnosis and treatment of skin diseases. To solve the problems of large variation of target size and shape in skin disease images, small difference between disease images and normal images, and difficulty of label acquisition, we propose a classification algorithm for skin lesion based on hybrid self‐supervised pretext tasks. We combine the discriminative self‐supervised relational reasoning task with the generative self‐supervised mutual information maximization task and design the loss function. Experiments show that the generative‐discriminative self‐supervised co‐training algorithm can identify images with small differences and learn discriminative features and achieve a classification accuracy of 82.6% on the DermaMNIST dataset.

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Section: Related Workmentioning

confidence: 99%