Multi-Label Retinal Disease Classification using Transformers

• URL: http://arxiv.org/abs/2207.02335v2
• Date: Thu, 7 Jul 2022 15:19:45 GMT
• Title: Multi-Label Retinal Disease Classification using Transformers
• Authors: M. A. Rodriguez, H. AlMarzouqi and P. Liatsis (Department of Electrical Engineering and Computer Science, Khalifa University)
• Abstract summary: A new multi-label retinal disease dataset, MuReD, is constructed, using a number of publicly available datasets for fundus disease classification. A transformer-based model optimized through extensive experimentation is used for image analysis and decision making. It is shown that the approach performs better than state-of-the-art works on the same task by 7.9% and 8.1% in terms of AUC score for disease detection and disease classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification. Next, a sequence of post-processing steps is applied to ensure the quality of the image data and the range of diseases, present in the dataset. For the first time in fundus multi-label disease classification, a transformer-based model optimized through extensive experimentation is used for image analysis and decision making. Numerous experiments are performed to optimize the configuration of the proposed system. It is shown that the approach performs better than state-of-the-art works on the same task by 7.9% and 8.1% in terms of AUC score for disease detection and disease classification, respectively. The obtained results further support the potential applications of transformer-based architectures in the medical imaging field.

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