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.

通过使用多任务少样本学习方法，我们提出了一种适应少量标记数据的多任务少样本学习方法，结合分割网络和分类网络，以实现对皮肤病变的自动分类和检测，提高皮肤癌的早期检测率。