2023/02/23 by Lucas Wannenmacher, Michael Fitzke, Wannenmacher, Lucas +5 · 1 citation
Computer Science · Medicine · #AI in cancer detection #Artificial Intelligence (cs.AI) #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.48550/arxiv.2302.11840
openalex publication_date 2023/02/23 · openalex created_date 2023/02/25 · openalex updated_date 2026/07/28
Chest X-ray images are commonly used in medical diagnosis, and AI models have been developed to assist with the interpretation of these images. However, many of these models rely on information from a single view of the X-ray, while multiple views may be available. In this work, we propose a novel approach for combining information from multiple views to improve the performance of X-ray image classification. Our approach is based on the use of a convolutional neural network to extract feature maps from each view, followed by an attention mechanism implemented using a Vision Transformer. The resulting model is able to perform multi-label classification on 41 labels and outperforms both single-view models and traditional multi-view classification architectures. We demonstrate the effectiveness of our approach through experiments on a dataset of 363,000 X-ray images.