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. 2020 Sep 30;2(5):e190183. doi: 10.1148/ryai.2020190183

Figure 4:

A, Without class weights, the model quickly learned large classes (brainstem and cerebrum) at the expense of small classes (central sulcus and septum pellucidum). However, it eventually converged when given sufficient training time. The y-axis represents soft Dice coefficients, which are the Dice coefficients of predicted softmax class probabilities before thresholding. B, Manual segmentation, C, predictions from model trained with attenuated weighting (model 3), D, predictions from model trained with balanced weighting (model 2), E, enlarged section from B (yellow dotted lines), and, F, enlarged section from D (yellow dotted lines). The red arrowheads in F indicate a thin layer of labeled voxels over the brain-cranium boundary. This thin layer was not seen in the unweighted model. See the section on “Effect of Class Weighting” for details.

A, Without class weights, the model quickly learned large classes (brainstem and cerebrum) at the expense of small classes (central sulcus and septum pellucidum). However, it eventually converged when given sufficient training time. The y-axis represents soft Dice coefficients, which are the Dice coefficients of predicted softmax class probabilities before thresholding. B, Manual segmentation, C, predictions from model trained with attenuated weighting (model 3), D, predictions from model trained with balanced weighting (model 2), E, enlarged section from B (yellow dotted lines), and, F, enlarged section from D (yellow dotted lines). The red arrowheads in F indicate a thin layer of labeled voxels over the brain-cranium boundary. This thin layer was not seen in the unweighted model. See the section on “Effect of Class Weighting” for details.