论文标题
这发生了变化：结合因果和非因果解释以产生胶囊内窥镜检查的疾病进展
This changes to that : Combining causal and non-causal explanations to generate disease progression in capsule endoscopy
论文作者
论文摘要
由于对了解深度学习网络的决策过程的不符合需要，因此模态依赖性和模型不合时宜的技术已经变得非常流行。尽管这两种想法都为自动决策提供了透明度，但大多数方法都专注于使用模态梯度（依赖模型）或使用模型的行为/结果（模型 - 诺斯替语）来忽略模型内部状态和推理。在这项工作中，我们提出了一种统一的解释方法，该方法给定一个实例结合了模型依赖性和不可知论的解释以产生解释集。生成的解释不仅在样本附近是一致的，而且可以突出图像内容与结果之间的因果关系。我们使用无线胶囊内窥镜（WCE）域来说明我们的解释的有效性。在SoftMax信息评分上，我们方法产生的显着图是可以比较或更好的。
Due to the unequivocal need for understanding the decision processes of deep learning networks, both modal-dependent and model-agnostic techniques have become very popular. Although both of these ideas provide transparency for automated decision making, most methodologies focus on either using the modal-gradients (model-dependent) or ignoring the model internal states and reasoning with a model's behavior/outcome (model-agnostic) to instances. In this work, we propose a unified explanation approach that given an instance combines both model-dependent and agnostic explanations to produce an explanation set. The generated explanations are not only consistent in the neighborhood of a sample but can highlight causal relationships between image content and the outcome. We use Wireless Capsule Endoscopy (WCE) domain to illustrate the effectiveness of our explanations. The saliency maps generated by our approach are comparable or better on the softmax information score.
