Pre-training on grayscale imagenet improves medical image classification

Abstract

Deep learning is quickly becoming the de facto standard approach for solving a range of medical image analysis tasks. However, large medical image datasets appropriate for training deep neural network models from scratch are difficult to assemble due to privacy restrictions and expert ground truth requirements, with typical open source datasets ranging from hundreds to thousands of images. A standard approach to counteract limited-size medical datasets is to pre-train models on large datasets in other domains, such as ImageNet for classification of natural images, before fine-tuning on the specific medical task of interest. However, ImageNet contains color images, which introduces artefacts and inefficiencies into models that are intended for single-channel medical images. To address this issue, we pre-trained an Inception-V3 model on ImageNet after converting the images to grayscale through a common transformation. Surprisingly, these models do not show a significant degradation in performance on the original ImageNet classification task, suggesting that color is not a critical feature of natural image classification. Furthermore, models pre-trained on grayscale ImageNet outperformed color ImageNet models in terms of both speed and accuracy when refined on disease classification from chest X-ray images.