Yan Hao, Wang Pingping, Jia Yetong, Si Xingyong, Wei Benzheng
Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, 266112, People's Republic of China.
Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, 266112, People's Republic of China.
Biomed Phys Eng Express. 2024 Aug 12;10(5). doi: 10.1088/2057-1976/ad644e.
Precise segmentation for skin cancer lesions at different stages is conducive to early detection and further treatment. Considering the huge cost of obtaining pixel-perfect annotations for this task, segmentation using less expensive image-level labels has become a research direction. Most image-level label weakly supervised segmentation uses class activation mapping (CAM) methods. A common consequence of this method is incomplete foreground segmentation, insufficient segmentation, or false negatives. At the same time, when performing weakly supervised segmentation of skin cancer lesions, ulcers, redness, and swelling may appear near the segmented areas of individual disease categories. This co-occurrence problem affects the model's accuracy in segmenting class-related tissue boundaries to a certain extent. The above two issues are determined by the loosely constrained nature of image-level labels that penalize the entire image space. Therefore, providing pixel-level constraints for weak supervision of image-level labels is the key to improving performance. To solve the above problems, this paper proposes a joint unsupervised constraint-assisted weakly supervised segmentation model (UCA-WSS). The weakly supervised part of the model adopts a dual-branch adversarial erasure mechanism to generate higher-quality CAM. The unsupervised part uses contrastive learning and clustering algorithms to generate foreground labels and fine boundary labels to assist segmentation and solve common co-occurrence problems in weakly supervised skin cancer lesion segmentation through unsupervised constraints. The model proposed in the article is evaluated comparatively with other related models on some public dermatology data sets. Experimental results show that our model performs better on the skin cancer segmentation task than other weakly supervised segmentation models, showing the potential of combining unsupervised constraint methods on weakly supervised segmentation.
对不同阶段的皮肤癌病变进行精确分割有助于早期检测和进一步治疗。考虑到为该任务获取像素完美标注的巨大成本，使用成本较低的图像级标签进行分割已成为一个研究方向。大多数图像级标签弱监督分割使用类激活映射（CAM）方法。这种方法的一个常见后果是前景分割不完整、分割不足或出现假阴性。同时，在对皮肤癌病变进行弱监督分割时，个别疾病类别的分割区域附近可能会出现溃疡、发红和肿胀等情况。这种共现问题在一定程度上影响了模型在分割类相关组织边界时的准确性。上述两个问题是由图像级标签的宽松约束性质决定的，这种性质会对整个图像空间进行惩罚。因此，为图像级标签的弱监督提供像素级约束是提高性能的关键。为了解决上述问题，本文提出了一种联合无监督约束辅助弱监督分割模型（UCA-WSS）。该模型的弱监督部分采用双分支对抗擦除机制来生成更高质量的CAM。无监督部分使用对比学习和聚类算法来生成前景标签和精细边界标签，以辅助分割，并通过无监督约束解决弱监督皮肤癌病变分割中常见的共现问题。本文提出的模型在一些公共皮肤病数据集上与其他相关模型进行了比较评估。实验结果表明，我们的模型在皮肤癌分割任务上比其他弱监督分割模型表现更好，显示了在弱监督分割中结合无监督约束方法的潜力。
