Shubham Kaushal, Yifan Sun, Ryan Zukerman, Royce W S Chen, Kaveri A Thakoor
{"title":"Detecting Eye Disease Using Vision Transformers Informed by Ophthalmology Resident Gaze Data<sup />.","authors":"Shubham Kaushal, Yifan Sun, Ryan Zukerman, Royce W S Chen, Kaveri A Thakoor","doi":"10.1109/EMBC40787.2023.10340746","DOIUrl":null,"url":null,"abstract":"<p><p>We showcase two proof-of-concept approaches for enhancing the Vision Transformer (ViT) model by integrating ophthalmology resident gaze data into its training. The resulting Fixation-Order-Informed ViT and Ophthalmologist-Gaze-Augmented ViT show greater accuracy and computational efficiency than ViT for detection of the eye disease, glaucoma.Clinical relevance- By enhancing glaucoma detection via our gaze-informed ViTs, we introduce a new paradigm for medical experts to directly interface with medical AI, leading the way for more accurate and interpretable AI 'teammates' in the ophthalmic clinic.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2023 ","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMBC40787.2023.10340746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We showcase two proof-of-concept approaches for enhancing the Vision Transformer (ViT) model by integrating ophthalmology resident gaze data into its training. The resulting Fixation-Order-Informed ViT and Ophthalmologist-Gaze-Augmented ViT show greater accuracy and computational efficiency than ViT for detection of the eye disease, glaucoma.Clinical relevance- By enhancing glaucoma detection via our gaze-informed ViTs, we introduce a new paradigm for medical experts to directly interface with medical AI, leading the way for more accurate and interpretable AI 'teammates' in the ophthalmic clinic.
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