Related papers: SwinCheX: Multi-label classification on chest X-ray images with transformers

SwinCheX: Multi-label classification on chest X-ray images with transformers

URL: http://arxiv.org/abs/2206.04246v1
Date: Thu, 9 Jun 2022 03:17:57 GMT
Title: SwinCheX: Multi-label classification on chest X-ray images with transformers
Authors: Sina Taslimi, Soroush Taslimi, Nima Fathi, Mohammadreza Salehi, Mohammad Hossein Rohban
Abstract summary: This paper proposes a multi-label classification deep model based on the Swin Transformer as the backbone to achieve state-of-the-art diagnosis classification. We evaluate our model on one of the most widely-used and largest x-ray datasets called "Chest X-ray14"
Score: 4.549831511476249
License: http://creativecommons.org/licenses/by/4.0/
Abstract: According to the considerable growth in the avail of chest X-ray images in diagnosing various diseases, as well as gathering extensive datasets, having an automated diagnosis procedure using deep neural networks has occupied the minds of experts. Most of the available methods in computer vision use a CNN backbone to acquire high accuracy on the classification problems. Nevertheless, recent researches show that transformers, established as the de facto method in NLP, can also outperform many CNN-based models in vision. This paper proposes a multi-label classification deep model based on the Swin Transformer as the backbone to achieve state-of-the-art diagnosis classification. It leverages Multi-Layer Perceptron, also known as MLP, for the head architecture. We evaluate our model on one of the most widely-used and largest x-ray datasets called "Chest X-ray14," which comprises more than 100,000 frontal/back-view images from over 30,000 patients with 14 famous chest diseases. Our model has been tested with several number of MLP layers for the head setting, each achieves a competitive AUC score on all classes. Comprehensive experiments on Chest X-ray14 have shown that a 3-layer head attains state-of-the-art performance with an average AUC score of 0.810, compared to the former SOTA average AUC of 0.799. We propose an experimental setup for the fair benchmarking of existing methods, which could be used as a basis for the future studies. Finally, we followed up our results by confirming that the proposed method attends to the pathologically relevant areas of the chest.

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