Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866916455257210880 |
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| author | Lambert, Benjamin Forbes, Florence Doyle, Senan Tucholka, Alan Dojat, Michel |
| author_facet | Lambert, Benjamin Forbes, Florence Doyle, Senan Tucholka, Alan Dojat, Michel |
| contents | Deep Learning models are easily disturbed by variations in the input images that were not seen during training, resulting in unpredictable behaviours. Such Out-of-Distribution (OOD) images represent a significant challenge in the context of medical image analysis, where the range of possible abnormalities is extremely wide, including artifacts, unseen pathologies, or different imaging protocols. In this work, we evaluate various uncertainty frameworks to detect OOD inputs in the context of Multiple Sclerosis lesions segmentation. By implementing a comprehensive evaluation scheme including 14 sources of OOD of various nature and strength, we show that methods relying on the predictive uncertainty of binary segmentation models often fails in detecting outlying inputs. On the contrary, learning to segment anatomical labels alongside lesions highly improves the ability to detect OOD inputs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2211_05421 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation Lambert, Benjamin Forbes, Florence Doyle, Senan Tucholka, Alan Dojat, Michel Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Deep Learning models are easily disturbed by variations in the input images that were not seen during training, resulting in unpredictable behaviours. Such Out-of-Distribution (OOD) images represent a significant challenge in the context of medical image analysis, where the range of possible abnormalities is extremely wide, including artifacts, unseen pathologies, or different imaging protocols. In this work, we evaluate various uncertainty frameworks to detect OOD inputs in the context of Multiple Sclerosis lesions segmentation. By implementing a comprehensive evaluation scheme including 14 sources of OOD of various nature and strength, we show that methods relying on the predictive uncertainty of binary segmentation models often fails in detecting outlying inputs. On the contrary, learning to segment anatomical labels alongside lesions highly improves the ability to detect OOD inputs. |
| title | Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2211.05421 |