PDF(Pubmed)
Abstract：
The emergence of unknown diseases is often with few or no samples available. Zero-shot learning and few-shot learning have promising applications in medical image analysis. In this paper, we propose a Cross-Modal Deep Metric Learning Generalized Zero-Shot Learning (CM-DML-GZSL) model. The proposed network consists of a visual feature extractor, a fixed semantic feature extractor, and a deep regression module. The network belongs to a two-stream network for multiple modalities. In a multi-label setting, each sample contains a small number of positive labels and a large number of negative labels on average. This positive-negative imbalance dominates the optimization procedure and may prevent the establishment of an effective correspondence between visual features and semantic vectors during training, resulting in a low degree of accuracy. A novel weighted focused Euclidean distance metric loss is introduced in this regard. This loss not only can dynamically increase the weight of hard samples and decrease the weight of simple samples, but it can also promote the connection between samples and semantic vectors corresponding to their positive labels, which helps mitigate bias in predicting unseen classes in the generalized zero-shot learning setting. The weighted focused Euclidean distance metric loss function can dynamically adjust sample weights, enabling zero-shot multi-label learning for chest X-ray diagnosis, as experimental results on large publicly available datasets demonstrate.
摘要：
未知疾病的出现通常很少或没有可用的样品。零射学习和少射学习在医学图像分析中具有广阔的应用前景。在本文中,我们提出了一种跨模态深度度量学习广义零分学习(CM-DML-GZSL)模型。拟议的网络由视觉特征提取器组成，固定的语义特征提取器，和深度回归模块。该网络属于用于多种模态的双流网络。在多标签设置中，每个样本平均包含少量阳性标签和大量阴性标签。这种正负不平衡主导了优化过程，并可能阻止在训练期间建立视觉特征和语义向量之间的有效对应关系。导致精度较低。在这方面引入了一种新颖的加权聚焦欧几里得距离度量损失。这种损失不仅可以动态增加硬质样品的重量，而且可以减少简单样品的重量，但它也可以促进样本和与其正标签相对应的语义向量之间的联系，这有助于减轻在广义零拍学习设置中预测看不见的类的偏差。加权聚焦欧氏距离度量损失函数可以动态调整样本权重,为胸部X光诊断提供零拍多标签学习，正如在大型公开数据集上的实验结果表明的那样。
