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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.14689 |
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| _version_ | 1866916581878005760 |
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| author | Ryabtsev, Dmitry Vasilyev, Boris Shershakov, Sergey |
| author_facet | Ryabtsev, Dmitry Vasilyev, Boris Shershakov, Sergey |
| contents | This paper introduces an innovative software system for fundus image analysis that deliberately diverges from the conventional screening approach, opting not to predict specific diagnoses. Instead, our methodology mimics the diagnostic process by thoroughly analyzing both normal and pathological features of fundus structures, leaving the ultimate decision-making authority in the hands of healthcare professionals. Our initiative addresses the need for objective clinical analysis and seeks to automate and enhance the clinical workflow of fundus image examination. The system, from its overarching architecture to the modular analysis design powered by artificial intelligence (AI) models, aligns seamlessly with ophthalmological practices. Our unique approach utilizes a combination of state-of-the-art deep learning methods and traditional computer vision algorithms to provide a comprehensive and nuanced analysis of fundus structures. We present a distinctive methodology for designing medical applications, using our system as an illustrative example. Comprehensive verification and validation results demonstrate the efficacy of our approach in revolutionizing fundus image analysis, with potential applications across various medical domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14689 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Approach to Designing CV Systems for Medical Applications: Data, Architecture and AI Ryabtsev, Dmitry Vasilyev, Boris Shershakov, Sergey Computer Vision and Pattern Recognition Artificial Intelligence This paper introduces an innovative software system for fundus image analysis that deliberately diverges from the conventional screening approach, opting not to predict specific diagnoses. Instead, our methodology mimics the diagnostic process by thoroughly analyzing both normal and pathological features of fundus structures, leaving the ultimate decision-making authority in the hands of healthcare professionals. Our initiative addresses the need for objective clinical analysis and seeks to automate and enhance the clinical workflow of fundus image examination. The system, from its overarching architecture to the modular analysis design powered by artificial intelligence (AI) models, aligns seamlessly with ophthalmological practices. Our unique approach utilizes a combination of state-of-the-art deep learning methods and traditional computer vision algorithms to provide a comprehensive and nuanced analysis of fundus structures. We present a distinctive methodology for designing medical applications, using our system as an illustrative example. Comprehensive verification and validation results demonstrate the efficacy of our approach in revolutionizing fundus image analysis, with potential applications across various medical domains. |
| title | Approach to Designing CV Systems for Medical Applications: Data, Architecture and AI |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2501.14689 |