Semi-Supervised Adversarial Transfer Learning for Automated Skin Lesion Segmentation

Ashish Bishnoi, A. Kannagi, Kalyan Acharjya
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引用次数: 0

Abstract

Semi-supervised adversarial transfer gaining knowledge of (SATL) has been proposed as a powerful method for automatic pores and skin lesion segmentation. This approach aims to transfer knowledge from a categorized supply area to an unlabeled target domain to enhance the segmentation accuracy. The approach uses a generative opposed community (GAN) to study a function area that's then used to switch the segmentation knowledge from the source to the target domain. Experiments have proven that SATL can enhance segmentation accuracy in the target domain by using as few as 2000 supply domain annotations. Usual, SATL provides a powerful method for automatic pores and skin lesion segmentation in domain names with limited amounts of labeled information and will probably revolutionize medical imaging diagnostics.
用于自动皮肤病变分割的半监督对抗转移学习
半监督对抗性知识转移(SATL)作为一种强大的自动毛孔和皮肤病变分割方法已被提出。这种方法旨在将知识从已分类的供应区转移到未标记的目标域,以提高分割准确性。该方法使用生成式对立群落(GAN)来研究一个功能区,然后利用该功能区将分割知识从源领域转换到目标领域。实验证明,SATL 只需使用 2000 个源域注释,就能提高目标域的分割准确性。通常,SATL 为在标注信息量有限的域名中自动进行毛孔和皮肤病变分割提供了一种强大的方法,并将可能彻底改变医学成像诊断。
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