Skin cancer is one of the deadliest diseases and has a high mortality rate if left untreated. The diagnosis generally starts with visual screening and is followed by a biopsy or histopathological examination. Early detection can aid in lowering mortality rates. Visual screening can be limited by the experience of the doctor. Due to the long tail distribution of dermatological datasets and significant intra-variability between classes, automatic classification utilizing computer-aided methods becomes challenging. In this work, we propose a multitask few-shot-based approach for skin lesions that generalizes well with few labelled data to address the small sample space challenge. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network. The output of the segmentation network helps to focus on the most discriminatory features while making a decision by the classification network. To further enhance the classification performance, we have combined segmentation and classification loss in a weighted manner. We have also included the visualization results that explain the decisions made by the algorithm. Three dermatological datasets are used to evaluate the proposed method thoroughly. We also conducted cross-database experiments to ensure that the proposed approach is generalizable across similar datasets. Experimental results demonstrate the efficacy of the proposed work.

翻译：皮肤癌是最致命的疾病之一，若未经治疗，死亡率极高。诊断通常始于视觉筛查，随后进行活检或组织病理学检查。早期检测有助于降低死亡率。视觉筛查可能受限于医生的经验。由于皮肤科数据集的长尾分布以及类别间显著的类内变异性，利用计算机辅助方法进行自动分类变得具有挑战性。本文针对小样本空间问题，提出了一种基于多任务少样本的皮肤病变分类方法，该方法在少量标注数据下具有良好的泛化能力。该方案融合了作为注意力模块的分割网络和分类网络。分割网络的输出有助于在分类网络做出决策时聚焦于最具判别力的特征。为进一步提升分类性能，我们以加权方式结合了分割损失和分类损失。此外，我们还提供了可视化结果，以解释算法做出的决策。使用三个皮肤科数据集对所提方法进行了全面评估，并进行了跨数据库实验以确保该方法在类似数据集上的泛化能力。实验结果证明了所提方法的有效性。