Recent years have witnessed a rapid development of automated methods for skin lesion diagnosis and classification. Due to an increasing deployment of such systems in clinics, it has become important to develop a more robust system towards various Out-of-Distribution(OOD) samples (unknown skin lesions and conditions). However, the current deep learning models trained for skin lesion classification tend to classify these OOD samples incorrectly into one of their learned skin lesion categories. To address this issue, we propose a simple yet strategic approach that improves the OOD detection performance while maintaining the multi-class classification accuracy for the known categories of skin lesion. To specify, this approach is built upon a realistic scenario of a long-tailed and fine-grained OOD detection task for skin lesion images. Through this approach, 1) First, we target the mixup amongst middle and tail classes to address the long-tail problem. 2) Later, we combine the above mixup strategy with prototype learning to address the fine-grained nature of the dataset. The unique contribution of this paper is two-fold, justified by extensive experiments. First, we present a realistic problem setting of OOD task for skin lesion. Second, we propose an approach to target the long-tailed and fine-grained aspects of the problem setting simultaneously to increase the OOD performance.
翻译:近年来,对皮肤损伤的诊断和分类自动化方法有了迅速的发展。由于在诊所越来越多地部署这种系统,因此有必要为各种外分发样本(已知的皮肤损伤和状况)开发一个更健全的系统。然而,目前为皮肤损伤分类而培训的深层学习模型往往将这些 OOD样本错误地分类为它们所学的皮肤损伤类别之一。为了解决这一问题,我们建议采取简单而战略性的办法,改进OOD检测性能,同时保持已知皮肤损伤类别的多级分类准确性。具体地说,这一方法建立在对皮肤损伤图像进行长尾部和细细细细的 OOD检测任务的现实情景之上。首先,我们把中尾部和尾部的混合作为目标,以解决长尾部问题。(2) 之后,我们把上述混合战略与原型学习结合起来,以解决数据集精细的特性。这份文件的独特贡献有两面,这是广泛的实验所证明的。首先,我们提出了将OODD检测目标放在皮肤方面,同时将OODA目标放在皮肤上。