Fast few-shot transfer learning for disease identification from chest x-ray images using autoencoder ensemble

We propose a fast few-shot learning framework that uses transfer learning to identify different lung and chest diseases and conditions from chest x-rays. Our model can be trained with as few as five training examples, making it potentially applicable for diagnosis of rare diseases. In this work, we divide different chest diseases into two disjoint categories: (i) base classes (with large training set) and (ii) novel classes (with a few training examples per class). Our method consists of two steps, namely feature extraction and classification. For the feature extraction, we employ a deep convolutional neural network, customized for chest x-rays. We train the feature extractor with data only from base classes. So the novel classes are unseen to the feature extractor during training. However, we use the feature extractor for extracting features from the data of novel classes resulting in transfer learning. Our classifier, on the other hand, uses the data only from the novel classes for training. We introduce the idea of autoencoder ensemble to design the classifier. Only a few feature vectors from each of the novel classes are used for training the classifier making it a few-shot learner. Incorporating new novel classes require training only the classifier which makes the entire process extremely fast. The performance of the classifier is evaluated on the test data from the novel classes. Experiments show _ 18% improvement in the F1 score compared to the baseline on identifying the novel diseases from publicly available chest x-ray dataset.