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Abstract #1791

Multi-Scale Evaluation of Uncertainty Quantification Techniques for Deep Learning based MRI Segmentation

Benjamin Lambert1,2, Florence Forbes3, Alan Tucholka2, Senan Doyle2, and Michel Dojat1
1Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France, 2Pixyl, Research and Development Laboratory, 38000, Grenoble, France, 3Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000, Grenoble, France


Efforts are required to design Deep Learning models that are not only powerful, but also capable of expressing the certainty of their predictions.
We evaluate 3 state-of-the-art techniques for uncertainty quantification : Monte-Carlo Dropout, Deep Ensemble, and Heteroscedastic models. Evaluation is illustrated on a task of automatic segmentation of White-Matter Hyperintensities in T2-weighted FLAIR MRI sequences of Multiple-Sclerosis patients. Analysis is performed at 3 different scales : the voxel, the lesion, and the whole image. Results indicate the superiority of the Heteroscedastic approach, which ranked first in both the uncertainty and segmentation tasks.

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