The Impact of Dataset Splits on Classification Performance in Medical Videos

Abstract

The creation of datasets in medical imaging is a central topic of research, especially with the advances of deep learning in the past decade. Publications of such datasets typically report baseline results with one or more deep neural networks in the form of established performance metrics (e.g., F1-score, Jaccard, etc.). Then, much work is done trying to beat these baseline metrics to compare different neural architectures. However, these reported metrics are almost meaningless when the underlying data does not conform to specific standards. In order to better understand what standards we need, we have reproduced and analyzed a study of four medical image classification datasets in laparoscopy. With automated frame extraction of surgical videos, we find that the resulting images are way too similar and produce high evaluation metrics by design. We show this similarity with a basic SIFT algorithm that produces high evaluation metrics on the original data. We confirm our hypothesis by creating and evaluating a video-based dataset split from the original images. The original network evaluated on the video-based split performs worse than our basic SIFT algorithm on the original data.