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001046032 0247_ $$2doi$$a10.1109/MIPRO65660.2025.11131920
001046032 037__ $$aFZJ-2025-03672
001046032 041__ $$aEnglish
001046032 1001_ $$0P:(DE-Juel1)185652$$aBusch, J. S.$$b0$$ufzj
001046032 1112_ $$a2025 MIPRO 48th ICT and Electronics Convention$$cOpatija$$d2025-06-02 - 2025-06-06$$gMIPRO2025$$wCroatia
001046032 245__ $$aLeveraging Vision Transformers with Hyperparameter Optimization for the Classification of Acute Respiratory Distress Syndrome
001046032 260__ $$bIEEE$$c2025
001046032 300__ $$a1293-1298
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001046032 520__ $$aAcute respiratory distress syndrome (ARDS) is a serious lung condition associated with a high mortality rate. The classification of this condition poses a challenge in intensive care medicine and diagnostic imaging. Artificial intelligence methods, particularly deep learning, can assist the diagnostic process. Recent advances have demonstrated the potential of vision transformers to improve image analysis through their ability to extract relevant features and complex patterns in images. In this study, a vision transformer is implemented for classifying ARDS in chest X-Rays using a two-step transfer learning approach. For this purpose, publicly available databases of X-rays are used, some of which have been annotated by a radiologist for the use case ARDS. Furthermore, in order to uncover the optimal combination of model parameters to streamline the training process, we implement a two-tier hyperparameter optimization using the Ray Tune framework on high-performance computing infrastructure. The retrained vision transformer was able to classify ARDS data with 95% accuracy, outperforming previous approaches employing residual networks. Our results highlight the improvement that can be achieved through a two-step transfer learning approach leveraging vision transformers and taking advantage of powerful supercomputing architecture. Ultimately, our work facilitates timely and accurate classification of ARDS thereby enabling improved outcomes for patients receiving critical care.
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001046032 588__ $$aDataset connected to CrossRef Conference
001046032 7001_ $$0P:(DE-Juel1)178934$$aBarakat, C. S.$$b1$$eCorresponding author$$ufzj
001046032 7001_ $$0P:(DE-HGF)0$$aPauli, T.$$b2
001046032 7001_ $$0P:(DE-HGF)0$$aFonck, S.$$b3
001046032 7001_ $$0P:(DE-HGF)0$$aStollenwerk, A.$$b4
001046032 7001_ $$0P:(DE-Juel1)185651$$aFritsch, S. J.$$b5$$ufzj
001046032 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b6$$ufzj
001046032 770__ $$z979-8-3315-3597-1
001046032 773__ $$a10.1109/MIPRO65660.2025.11131920
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001046032 9141_ $$y2025
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