Deep learning machine models are employed for the segmentation of tumors and pathological changes in medical images. This paper presents the results of an analysis, based on selected metrics, regarding the U-Net, U2-Net, U-Net 3+, and TransUnet models. Furthermore, it discusses how the selected models address the issue of data heterogeneity.
Trained models can be downloaded from OneDrive. You have to be member of the Silesian University of Science organization in order to access those models.
| Metryki | U-Net | U$^2$-Net | U-Net 3+ | TransUnet |
|---|---|---|---|---|
| Focal Tversky | 0.278 +- 2.03e-02 | 0.266 +- 7.24e-03 | 0.305 +- 6.75e-03 | 0.316 +- 2.39e-02 |
| Dokładność | 0.972 +- 3.73e-03 | 0.973 +- 1.90e-03 | 0.969 +- 1.94e-03 | 0.965 +- 3.73e-03 |
| Średnia dokładność | 0.86 +- 1.64e-02 | 0.864 +- 1.10e-02 | 0.852 +- 1.20e-02 | 0.833 +- 1.75e-02 |
| Precyzja | 0.729 +- 3.22e-02 | 0.736 +- 2.22e-02 | 0.715 +- 2.54e-02 | 0.677 +- 3.47e-02 |
| Czułość | 0.864 +- 1.03e-02 | 0.881 +- 5.84e-03 | 0.839 +- 2.32e-02 | 0.843 +- 2.53e-02 |
| F1/Dice | 0.79 +- 2.29e-02 | 0.801 +- 1.12e-02 | 0.769 +- 6.76e-03 | 0.749 +- 2.43e-02 |
| IoU | 0.655 +- 3.01e-02 | 0.669 +- 1.58e-02 | 0.625 +- 8.81e-03 | 0.601 +- 3.21e-02 |
| ROC AUC | 0.946 +- 6.29e-03 | 0.988 +- 6.18e-04 | 0.932 +- 6.76e-03 | 0.96 +- 5.72e-03 |
| Metryki | U-Net | U$^2$-Net | U-Net 3+ | TransUnet |
|---|---|---|---|---|
| Focal Tversky | 0.366 +- 1.40e-02 | 0.38 +- 7.26e-03 | 0.359 +- 3.30e-03 | 0.344 +- 1.22e-02 |
| Dokładność | 0.945 +- 3.13e-03 | 0.947 +- 4.21e-03 | 0.947 +- 2.08e-03 | 0.945 +- 6.15e-03 |
| Średnia dokładność | 0.8 +- 9.00e-03 | 0.812 +- 1.43e-02 | 0.809 +- 8.16e-03 | 0.805 +- 1.80e-02 |
| Precyzja | 0.616 +- 1.74e-02 | 0.644 +- 2.93e-02 | 0.634 +- 1.76e-02 | 0.625 +- 3.75e-02 |
| Czułość | 0.807 +- 1.42e-02 | 0.774 +- 1.09e-02 | 0.809 +- 1.56e-02 | 0.845 +- 2.48e-02 |
| F1/Dice | 0.699 +- 1.43e-02 | 0.7 +- 1.37e-02 | 0.709 +- 4.71e-03 | 0.713 +- 1.86e-02 |
| IoU | 0.538 +- 1.68e-02 | 0.54 +- 1.62e-02 | 0.55 +- 5.69e-03 | 0.555 +- 2.20e-02 |
| ROC AUC | 0.895 +- 8.19e-03 | 0.961 +- 1.60e-03 | 0.907 +- 5.08e-03 | 0.938 +- 3.22e-03 |
| Metryki | U-Net | U$^2$-Net | U-Net 3+ | TransUnet |
|---|---|---|---|---|
| Focal Tversky | 0.572 +- 3.35e-02 | 0.67 +- 1.99e-02 | 0.641 +- 6.64e-02 | 0.615 +- 2.55e-02 |
| Dokładność | 0.802 +- 3.52e-02 | 0.646 +- 3.93e-02 | 0.663 +- 7.46e-02 | 0.734 +- 3.98e-02 |
| Średnia dokładność | 0.638 +- 2.12e-02 | 0.587 +- 7.22e-03 | 0.61 +- 3.72e-02 | 0.609 +- 1.09e-02 |
| Precyzja | 0.291 +- 4.34e-02 | 0.181 +- 1.49e-02 | 0.234 +- 7.45e-02 | 0.229 +- 2.26e-02 |
| Czułość | 0.847 +- 2.27e-02 | 0.94 +- 8.59e-03 | 0.894 +- 2.72e-02 | 0.912 +- 1.28e-02 |
| F1/Dice | 0.424 +- 4.34e-02 | 0.303 +- 2.07e-02 | 0.346 +- 7.94e-02 | 0.363 +- 2.86e-02 |
| IoU | 0.273 +- 3.57e-02 | 0.179 +- 1.43e-02 | 0.222 +- 6.59e-02 | 0.223 +- 2.10e-02 |
| ROC AUC | 0.835 +- 1.56e-02 | 0.908 +- 4.07e-03 | 0.839 +- 2.54e-02 | 0.825 +- 1.57e-02 |
| Metryki | U-Net | U$^2$-Net | U-Net 3+ | TransUnet |
|---|---|---|---|---|
| Focal Tversky | 0.391 +- 2.27e-02 | 0.434 +- 1.91e-02 | 0.398 +- 2.74e-02 | 0.381 +- 1.48e-02 |
| Dokładność | 0.965 +- 3.02e-03 | 0.96 +- 3.40e-03 | 0.967 +- 1.26e-03 | 0.962 +- 3.99e-03 |
| Średnia dokładność | 0.848 +- 1.47e-02 | 0.827 +- 1.81e-02 | 0.867 +- 1.28e-02 | 0.832 +- 2.12e-02 |
| Precyzja | 0.715 +- 2.89e-02 | 0.675 +- 3.58e-02 | 0.754 +- 2.75e-02 | 0.679 +- 4.39e-02 |
| Czułość | 0.715 +- 2.41e-02 | 0.677 +- 2.02e-02 | 0.694 +- 4.43e-02 | 0.754 +- 2.96e-02 |
| F1/Dice | 0.713 +- 2.22e-02 | 0.674 +- 2.22e-02 | 0.717 +- 1.49e-02 | 0.708 +- 1.72e-02 |
| IoU | 0.556 +- 2.72e-02 | 0.51 +- 2.62e-02 | 0.559 +- 1.85e-02 | 0.548 +- 2.03e-02 |
| ROC AUC | 0.899 +- 1.81e-02 | 0.959 +- 7.84e-03 | 0.866 +- 2.16e-02 | 0.945 +- 4.32e-03 |
| Metryki | U-Net | U$^2$-Net | U-Net 3+ | TransUnet |
|---|---|---|---|---|
| Focal Tversky | 0.316 +- 2.75e-02 | 0.256 +- 1.41e-02 | 0.278 +- 6.06e-03 | 0.254 +- 8.75e-03 |
| Dokładność | 0.959 +- 7.47e-03 | 0.976 +- 1.10e-03 | 0.97 +- 1.27e-03 | 0.975 +- 1.43e-03 |
| Średnia dokładność | 0.812 +- 2.71e-02 | 0.879 +- 5.15e-03 | 0.849 +- 7.37e-03 | 0.868 +- 7.17e-03 |
| Precyzja | 0.633 +- 5.42e-02 | 0.767 +- 1.05e-02 | 0.707 +- 1.54e-02 | 0.744 +- 1.43e-02 |
| Czułość | 0.882 +- 1.01e-02 | 0.875 +- 1.83e-02 | 0.88 +- 1.38e-02 | 0.888 +- 7.89e-03 |
| F1/Dice | 0.731 +- 3.63e-02 | 0.817 +- 9.07e-03 | 0.783 +- 5.91e-03 | 0.809 +- 9.08e-03 |
| IoU | 0.581 +- 4.47e-02 | 0.691 +- 1.31e-02 | 0.644 +- 7.90e-03 | 0.68 +- 1.30e-02 |
| ROC AUC | 0.932 +- 4.91e-03 | 0.989 +- 1.19e-03 | 0.942 +- 1.24e-02 | 0.97 +- 2.75e-03 |
| Metryki | U-Net | U$^2$-Net | U-Net 3+ | TransUnet |
|---|---|---|---|---|
| Focal Tversky | 0.386 +- 3.43e-02 | 0.333 +- 7.39e-03 | 0.364 +- 9.64e-03 | 0.322 +- 4.21e-03 |
| Dokładność | 0.934 +- 9.97e-03 | 0.956 +- 2.28e-03 | 0.947 +- 2.25e-03 | 0.956 +- 1.94e-03 |
| Średnia dokładność | 0.776 +- 2.42e-02 | 0.837 +- 8.81e-03 | 0.807 +- 9.20e-03 | 0.833 +- 7.65e-03 |
| Precyzja | 0.568 +- 4.68e-02 | 0.691 +- 1.84e-02 | 0.631 +- 1.99e-02 | 0.681 +- 1.60e-02 |
| Czułość | 0.814 +- 3.28e-02 | 0.812 +- 1.48e-02 | 0.803 +- 2.36e-02 | 0.831 +- 1.04e-02 |
| F1/Dice | 0.666 +- 3.85e-02 | 0.745 +- 8.41e-03 | 0.705 +- 6.68e-03 | 0.748 +- 6.65e-03 |
| IoU | 0.504 +- 4.15e-02 | 0.594 +- 1.07e-02 | 0.544 +- 7.92e-03 | 0.598 +- 8.43e-03 |
| ROC AUC | 0.895 +- 1.35e-02 | 0.969 +- 1.61e-03 | 0.897 +- 1.90e-02 | 0.933 +- 6.79e-03 |
