Skin cancer detection through dermoscopy image analysis is a critical task.
However, existing models used for this purpose often lack interpretability and
reliability, raising the concern of physicians due to their black-box nature.
In this paper, we propose a novel approach for the diagnosis of melanoma using
an interpretable prototypical-part model. We introduce a guided supervision
based on non-expert feedback through the incorporation of: 1) binary masks,
obtained automatically using a segmentation network; and 2) user-refined
prototypes. These two distinct information pathways aim to ensure that the
learned prototypes correspond to relevant areas within the skin lesion,
excluding confounding factors beyond its boundaries. Experimental results
demonstrate that, even without expert supervision, our approach achieves
superior performance and generalization compared to non-interpretable models.

通过可解释的原型 - 部分模型以及自动分割网络和用户优化的原型引入的非专家反馈指导，我们提出了一种新的黑素瘤诊断方法，实验证明即使没有专家监督，我们的方法在性能和泛化程度上均优于不可解释的模型。

利用原型和非专业监督进行皮肤癌检测的可解释人工智能（XAI）

XAI for Skin Cancer Detection with Prototypes and Non-Expert Supervision

Skin cancer is a serious worldwide health issue, precise and early detection
is essential for better patient outcomes and effective treatment. In this
research, we use modern deep learning methods and explainable artificial
intelligence (XAI) approaches to address the problem of skin cancer detection.
To categorize skin lesions, we employ four cutting-edge pre-trained models:
XceptionNet, EfficientNetV2S, InceptionResNetV2, and EfficientNetV2M. Image
augmentation approaches are used to reduce class imbalance and improve the
generalization capabilities of our models. Our models decision-making process
can be clarified because of the implementation of explainable artificial
intelligence (XAI). In the medical field, interpretability is essential to
establish credibility and make it easier to implement AI driven diagnostic
technologies into clinical workflows. We determined the XceptionNet
architecture to be the best performing model, achieving an accuracy of 88.72%.
Our study shows how deep learning and explainable artificial intelligence (XAI)
can improve skin cancer diagnosis, laying the groundwork for future
developments in medical image analysis. These technologies ability to allow for
early and accurate detection could enhance patient care, lower healthcare
costs, and raise the survival rates for those with skin cancer. Source Code:
this https URL

使用现代深度学习方法和可解释人工智能（XAI）来解决皮肤癌检测问题，采用四个先进的预训练模型进行皮肤病变分类，XceptionNet 被确定为表现最佳的模型，本研究展示了深度学习和可解释人工智能（XAI）如何改善皮肤癌诊断，为医学图像分析领域未来的发展奠定了基础。

皮肤癌分类的可解释深度学习方法

An Interpretable Deep Learning Approach for Skin Cancer Categorization

Skin cancer affects a large population every year -- automated skin cancer
detection algorithms can thus greatly help clinicians. Prior efforts involving
deep learning models have high detection accuracy. However, most of the models
have a large number of parameters, with some works even using an ensemble of
models to achieve good accuracy. In this paper, we investigate a recently
proposed pruning technique called Lottery Ticket Hypothesis. We find that
iterative pruning of the network resulted in improved accuracy, compared to
that of the unpruned network, implying that -- the lottery ticket hypothesis
can be applied to the problem of skin cancer detection and this hypothesis can
result in a smaller network for inference. We also examine the accuracy across
sub-groups -- created by gender and age -- and it was found that some
sub-groups show a larger increase in accuracy than others.

本文通过采用 Lottery Ticket Hypothesis 修剪算法，对深度学习模型在皮肤癌检测领域中的应用进行了研究，证明使用该算法可以获得更小的神经网络并提高准确率，在性别和年龄等细分领域的部分小组中准确率的提高更为显著。