Automated dermoscopic image analysis has witnessed rapid growth in diagnostic
performance. Yet adoption faces resistance, in part, because no evidence is
provided to support decisions. In this work, an approach for evidence-based
classification is presented. A feature embedding is learned with CNNs,
triplet-loss, and global average pooling, and used to classify via kNN search.
Evidence is provided as both the discovered neighbors, as well as localized
image regions most relevant to measuring distance between query and neighbors.
To ensure that results are relevant in terms of both label accuracy and human
visual similarity for any skill level, a novel hierarchical triplet logic is
implemented to jointly learn an embedding according to disease labels and
non-expert similarity. Results are improved over baselines trained on disease
labels alone, as well as standard multiclass loss. Quantitative relevance of
results, according to non-expert similarity, as well as localized image
regions, are also significantly improved.

提出一种基于证据的分类方法，利用 CNN，三元损失和全局平均池化学习特征嵌入，通过 kNN 搜索分类，并以发现的邻居和最相关的局部图像区域作为证据。通过实现一种新的分级三元逻辑，根据疾病标签和非专家相似性一起学习嵌入，以确保结果对于任何技能水平的人来说都具有标签准确性和视觉相似性，从而改善了基于疾病标签和标准多类损失的基准结果，并显着改善了结果的定量相关性和局部图像区域。