Abstract

This article describes PUC Chile team's participation in the Caption Prediction task of ImageCLEFmedical challenge 2021, which resulted in the team winning this task. We first show how a very simple approach based on statistical analysis of captions, without relying on images, results in a competitive baseline score. Then, we describe how to improve the performance of this preliminary submission by encoding the medical images with a ResNet CNN, pre-trained on ImageNet and later fine-tuned with the challenge dataset. Afterwards, we use this visual encoding as the input for a multi-label classification approach for caption prediction. We describe in detail our final approach, and we conclude by discussing some ideas for future work.