Sanner, Antoine ; González, Camila ; Mukhopadhyay, Anirban (2021)
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?
43rd DAGM German Conference on Pattern Recognition (DAGM GCPR 2021). virtual Conference (28.09.2021-01.10.2021)
doi: 10.1007/978-3-030-92659-5_39
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The recent achievements of Deep Learning rely on the test data being similar in distribution to the training data. In an ideal case, Deep Learning models would achieve Out-of-Distribution (OoD) Generalization, i.e. reliably make predictions on out-of-distribution data. Yet in practice, models usually fail to generalize well when facing a shift in distribution. Several methods were thereby designed to improve the robustness of the features learned by a model through Regularization- or Domain-Prediction-based schemes. Segmenting medical images such as MRIs of the hippocampus is essential for the diagnosis and treatment of neuropsychiatric disorders. But these brain images often suffer from distribution shift due to the patient’s age and various pathologies affecting the shape of the organ. In this work, we evaluate OoD Generalization solutions for the problem of hippocampus segmentation in MR data using both fully- and semi-supervised training. We find that no method performs reliably in all experiments. Only the V-REx loss stands out as it remains easy to tune, while it outperforms a standard U-Net in most cases.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Sanner, Antoine ; González, Camila ; Mukhopadhyay, Anirban |
| Art des Eintrags: | Bibliographie |
| Titel: | How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation? |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Verlag: | Springer |
| Buchtitel: | Pattern Recognition |
| Reihe: | Lecture Notes in Computer Science |
| Band einer Reihe: | 13024 |
| Veranstaltungstitel: | 43rd DAGM German Conference on Pattern Recognition (DAGM GCPR 2021) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 28.09.2021-01.10.2021 |
| DOI: | 10.1007/978-3-030-92659-5_39 |
| Kurzbeschreibung (Abstract): | The recent achievements of Deep Learning rely on the test data being similar in distribution to the training data. In an ideal case, Deep Learning models would achieve Out-of-Distribution (OoD) Generalization, i.e. reliably make predictions on out-of-distribution data. Yet in practice, models usually fail to generalize well when facing a shift in distribution. Several methods were thereby designed to improve the robustness of the features learned by a model through Regularization- or Domain-Prediction-based schemes. Segmenting medical images such as MRIs of the hippocampus is essential for the diagnosis and treatment of neuropsychiatric disorders. But these brain images often suffer from distribution shift due to the patient’s age and various pathologies affecting the shape of the organ. In this work, we evaluate OoD Generalization solutions for the problem of hippocampus segmentation in MR data using both fully- and semi-supervised training. We find that no method performs reliably in all experiments. Only the V-REx loss stands out as it remains easy to tune, while it outperforms a standard U-Net in most cases. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 24 Mai 2022 07:11 |
| Letzte Änderung: | 15 Nov 2022 11:56 |
| PPN: | 501671765 |
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