论文标题
可解释的显着性图和自我监督的学习，用于广义零射击医学图像分类
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification
论文作者
论文摘要
在许多现实世界的医学图像分类设置中，我们无法访问所有可能的疾病类别的样本，而强大的系统有望在识别新型测试数据方面具有高性能。我们提出了一种使用自我监督学习（SSL）的广义零射击学习（GZSL）方法：1）选择不同疾病类别的锚定向量； 2）培训功能生成器。我们的方法不需要类属性向量，这些向量可用于自然图像，但不适合医学图像。 SSL确保锚向量代表每个类别。 SSL还用于生成看不见类的合成特征。使用更简单的架构，我们的方法与基于SSL的最先进的GZSL方法匹配自然图像，并且优于医学图像的所有方法。我们的方法足够适应于自然图像时可容纳类属性向量。
In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized zero shot learning (GZSL) method that uses self supervised learning (SSL) for: 1) selecting anchor vectors of different disease classes; and 2) training a feature generator. Our approach does not require class attribute vectors which are available for natural images but not for medical images. SSL ensures that the anchor vectors are representative of each class. SSL is also used to generate synthetic features of unseen classes. Using a simpler architecture, our method matches a state of the art SSL based GZSL method for natural images and outperforms all methods for medical images. Our method is adaptable enough to accommodate class attribute vectors when they are available for natural images.
