Expert-aware uncertainty estimation for quality control of neural-based blood typing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910528828342272 |
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| author | Zaychenkova, Ekaterina Iarchuk, Dmitrii Korchagin, Sergey Zaitsev, Alexey Ershov, Egor |
| author_facet | Zaychenkova, Ekaterina Iarchuk, Dmitrii Korchagin, Sergey Zaitsev, Alexey Ershov, Egor |
| contents | In medical diagnostics, accurate uncertainty estimation for neural-based models is essential for complementing second-opinion systems. Despite neural network ensembles' proficiency in this problem, a gap persists between actual uncertainties and predicted estimates. A major difficulty here is the lack of labels on the hardness of examples: a typical dataset includes only ground truth target labels, making the uncertainty estimation problem almost unsupervised. Our novel approach narrows this gap by integrating expert assessments of case complexity into the neural network's learning process, utilizing both definitive target labels and supplementary complexity ratings. We validate our methodology for blood typing, leveraging a new dataset "BloodyWell" unique in augmenting labeled reaction images with complexity scores from six medical specialists. Experiments demonstrate enhancement of our approach in uncertainty prediction, achieving a 2.5-fold improvement with expert labels and a 35% increase in performance with estimates of neural-based expert consensus. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11181 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Expert-aware uncertainty estimation for quality control of neural-based blood typing Zaychenkova, Ekaterina Iarchuk, Dmitrii Korchagin, Sergey Zaitsev, Alexey Ershov, Egor Computer Vision and Pattern Recognition In medical diagnostics, accurate uncertainty estimation for neural-based models is essential for complementing second-opinion systems. Despite neural network ensembles' proficiency in this problem, a gap persists between actual uncertainties and predicted estimates. A major difficulty here is the lack of labels on the hardness of examples: a typical dataset includes only ground truth target labels, making the uncertainty estimation problem almost unsupervised. Our novel approach narrows this gap by integrating expert assessments of case complexity into the neural network's learning process, utilizing both definitive target labels and supplementary complexity ratings. We validate our methodology for blood typing, leveraging a new dataset "BloodyWell" unique in augmenting labeled reaction images with complexity scores from six medical specialists. Experiments demonstrate enhancement of our approach in uncertainty prediction, achieving a 2.5-fold improvement with expert labels and a 35% increase in performance with estimates of neural-based expert consensus. |
| title | Expert-aware uncertainty estimation for quality control of neural-based blood typing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.11181 |