An Ordinal Regression Framework for a Deep Learning Based Severity Assessment for Chest Radiographs
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909099084480512 |
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| author | Wienholt, Patrick Hermans, Alexander Khader, Firas Puladi, Behrus Leibe, Bastian Kuhl, Christiane Nebelung, Sven Truhn, Daniel |
| author_facet | Wienholt, Patrick Hermans, Alexander Khader, Firas Puladi, Behrus Leibe, Bastian Kuhl, Christiane Nebelung, Sven Truhn, Daniel |
| contents | This study investigates the application of ordinal regression methods for categorizing disease severity in chest radiographs. We propose a framework that divides the ordinal regression problem into three parts: a model, a target function, and a classification function. Different encoding methods, including one-hot, Gaussian, progress-bar, and our soft-progress-bar, are applied using ResNet50 and ViT-B-16 deep learning models. We show that the choice of encoding has a strong impact on performance and that the best encoding depends on the chosen weighting of Cohen's kappa and also on the model architecture used. We make our code publicly available on GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05685 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Ordinal Regression Framework for a Deep Learning Based Severity Assessment for Chest Radiographs Wienholt, Patrick Hermans, Alexander Khader, Firas Puladi, Behrus Leibe, Bastian Kuhl, Christiane Nebelung, Sven Truhn, Daniel Computer Vision and Pattern Recognition This study investigates the application of ordinal regression methods for categorizing disease severity in chest radiographs. We propose a framework that divides the ordinal regression problem into three parts: a model, a target function, and a classification function. Different encoding methods, including one-hot, Gaussian, progress-bar, and our soft-progress-bar, are applied using ResNet50 and ViT-B-16 deep learning models. We show that the choice of encoding has a strong impact on performance and that the best encoding depends on the chosen weighting of Cohen's kappa and also on the model architecture used. We make our code publicly available on GitHub. |
| title | An Ordinal Regression Framework for a Deep Learning Based Severity Assessment for Chest Radiographs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.05685 |