Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Architectures and Training Parameters
2.3. Loss functions
- , or true positives (TP): polyp pixels in the ground truth binary mask that are correctly classified as polyp in the predicted binary mask.
- , or false negatives (FN): polyp pixels in the ground truth binary mask that are incorrectly classified as background in the predicted binary mask.
- , or false positives (FP): background pixels in the ground truth binary mask that are incorrectly classified as polyp in the predicted binary mask.
- , or true negatives (TN): background pixels in the ground truth binary mask that are correctly classified as background in the predicted binary mask.
2.3.1. Jaccard Loss
2.3.2. Dice Loss
2.3.3. Binary Cross Entropy Loss
2.3.4. Binary Focal Loss
2.3.5. Tversky Loss
2.3.6. Focal Tversky Loss
2.3.7. Lovász-Hinge Loss
2.4. Metrics
2.5. PCA Analysis
3. Results and Analysis
3.1. PCA Analysis
- Sum: where all coefficients are equal to 1.
- Mean: where all coefficients are equal to .
- Normalized eigenloss: where coefficients are normalized using the formula.
3.2. Training and Testing Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Loss Function | Accuracy | Precision | Recall | Specificity | F2-Score | Jaccard | Dice |
---|---|---|---|---|---|---|---|
Jaccard | 93.36 ± 10.26 | 88.69 ± 23.02 | 65.65 ± 37.52 | 99.24 ± 1.73 | 65.79 ± 36.20 | 59.77 ± 35.03 | 67.39 ± 34.44 |
Dice | 92.76 ± 10.45 | 82.20 ± 27.36 | 63.24 ± 37.94 | 99.07 ± 1.66 | 63.23 ± 36.42 | 56.38 ± 34.59 | 64.61 ± 34.47 |
Binary entropy | 93.18 ± 10.28 | 85.39 ± 30.95 | 64.62 ± 38.92 | 99.29 ± 1.45 | 64.89 ± 37.90 | 59.48 ± 36.35 | 66.39 ± 36.58 |
Binary focal | 93.34 ± 10.28 | 82.44 ± 26.45 | 68.84 ± 36.37 | 98.59 ± 2.84 | 68.13 ± 34.91 | 60.49 ± 33.66 | 68.48 ± 33.59 |
Tversky | 92.80 ± 10.53 | 82.80 ± 29.18 | 60.84 ± 39.16 | 99.22 ± 1.57 | 61.20 ± 38.03 | 55.34 ± 36.12 | 62.88 ± 36.59 |
Focal Tversky | 93.10 ± 9.94 | 81.19 ± 26.54 | 64.03 ± 38.72 | 98.98 ± 2.13 | 63.81 ± 37.25 | 57.20 ± 35.57 | 64.89 ± 35.46 |
Lovász-Hinge | 91.26 ± 11.07 | 72.26 ± 41.71 | 40.09 ± 39.55 | 99.71 ± 0.86 | 41.48 ± 39.44 | 38.23 ± 37.64 | 44.74 ± 39.49 |
Sum | 92.78 ± 10.45 | 81.15 ± 32.97 | 57.76 ± 39.88 | 99.37 ± 1.44 | 58.43 ± 39.01 | 53.58 ± 37.44 | 60.64 ± 38.11 |
Mean | 93.33 ± 10.5 | 84.07 ± 29.03 | 69.53 ± 35.64 | 98.84 ± 2.42 | 69.38 ± 34.42 | 62.83 ± 33.45 | 70.49 ± 33.13 |
Eigenloss | 93.00 ± 10.91 | 80.31 ± 31.35 | 65.07 ± 37.70 | 99.00 ± 2.25 | 65.45 ± 36.70 | 59.66 ± 35.32 | 67.04 ± 35.39 |
Norm Eigenloss | 92.97 ± 10.13 | 81.62 ± 27.02 | 64.02 ± 37.73 | 98.91 ± 2.06 | 63.82 ± 35.99 | 56.56 ± 33.99 | 65.01 ± 33.99 |
Loss Function | Accuracy | Precision | Recall | Specificity | F2-Score | Jaccard | Dice |
---|---|---|---|---|---|---|---|
Jaccard | 95.02 ± 9.53 | 86.95 ± 23.58 | 79.18 ± 33.08 | 99.14 ± 1.66 | 78.56 ± 31.94 | 72.17 ± 31.06 | 78.45 ± 30.37 |
Dice | 94.17 ± 10.22 | 90.10 ± 24.86 | 77.88 ± 33.74 | 98.99 ± 2.34 | 76.63 ± 32.63 | 69.88 ± 32.63 | 76.30 ± 31.61 |
Binary entropy | 94.30 ± 9.68 | 86.58 ± 23.06 | 75.60 ± 33.75 | 99.12 ± 1.65 | 74.96 ± 32.17 | 68.07 ± 31.89 | 75.39 ± 30.06 |
Binary focal | 94.27 ± 9.81 | 84.61 ± 22.94 | 80.84 ± 31.62 | 98.64 ± 2.67 | 78.69 ± 30.03 | 70.01 ± 30.18 | 77.40 ± 28.41 |
Tversky | 94.74 ± 9.77 | 89.04 ± 19.81 | 81.04 ± 30.35 | 99.02 ± 2.29 | 79.71 ± 29.35 | 73.19 ± 30.17 | 79.72 ± 27.98 |
Focal Tversky | 94.19 ± 9.83 | 83.08 ± 25.12 | 78.59 ± 34.18 | 98.62 ± 2.90 | 68.05 ± 32.22 | 75.10 ± 31.24 | 79.84 ± 24.73 |
Lovász-Hinge | 94.73 ± 9.77 | 87.52 ± 21.59 | 80.07 ± 31.72 | 98.95 ± 2.37 | 79.01 ± 30.42 | 72.26 ± 30.62 | 78.86 ± 28.78 |
Sum | 94.76 ± 9.53 | 87.92 ± 22.08 | 80.72 ± 30.32 | 98.95 ± 2.38 | 79.69 ± 29.12 | 72.89 ± 29.77 | 79.62 ± 27.80 |
Mean | 94.45 ± 9.77 | 86.12 ± 22.40 | 80.45 ± 31.49 | 98.82 ± 2.56 | 78.48 ± 30.17 | 70.75 ± 30.84 | 77.72 ± 28.96 |
Eigenloss | 94.43 ± 9.78 | 89.83 ± 21.63 | 77.39 ± 33.28 | 99.06 ± 2.18 | 76.41 ± 31.97 | 69.35 ± 31.55 | 76.33 ± 30.39 |
Norm Eigenloss | 94.68 ± 9.69 | 87.85 ± 22.12 | 79.24 ± 32.31 | 99.09 ± 1.95 | 78.43 ± 31.11 | 71.86 ± 31.02 | 78.37 ± 29.60 |
Loss Function | Accuracy | Precision | Recall | Specificity | F2-Score | Jaccard | Dice |
---|---|---|---|---|---|---|---|
Jaccard | 93.41 ± 10.47 | 82.86 ± 30.16 | 68.2 ± 36.67 | 99.34 ± 1.22 | 68.51 ± 35.56 | 62.75 ± 34.19 | 70.07 ± 34.11 |
Dice | 93.59 ± 10.38 | 81.03 ± 32.94 | 67.58 ± 38.01 | 99.49 ± 0.96 | 67.90 ± 37.10 | 62.53 ± 35.45 | 69.24 ± 35.92 |
Binary entropy | 93.61 ± 9.98 | 83.01 ± 26.67 | 70.18 ± 35.97 | 98.97 ± 1.97 | 69.76 ± 34.50 | 62.65 ± 33.05 | 70.48 ± 32.88 |
Binary focal | 93.15 ± 10.28 | 81.04 ± 28.02 | 66.18 ± 38.02 | 98.95 ± 1.76 | 65.63 ± 36.44 | 58.36 ± 34.85 | 66.22 ± 34.65 |
Tversky | 93.45 ± 10.78 | 82.54 ± 28.10 | 69.82 ± 37.05 | 99.13 ± 1.85 | 69.55 ± 35.85 | 63.14 ± 34.49 | 70.27 ± 34.33 |
Focal Tversky | 93.56 ± 10.35 | 83.56 ± 26.75 | 70.18 ± 37.39 | 99.09 ± 1.74 | 69.54 ± 36.10 | 62.81 ± 34.66 | 69.91 ± 34.58 |
Lovász-Hinge | 92.98 ± 10.28 | 80.57 ± 29.84 | 68.17 ± 37.82 | 98.73 ± 2.60 | 67.21 ± 36.06 | 59.6 ± 34.49 | 67.42 ± 34.23 |
Sum | 93.26 ± 10.08 | 78.00 ± 32.57 | 68.06 ± 36.90 | 98.76 ± 2.59 | 67.53 ± 35.50 | 60.4 ± 34.20 | 68.17 ± 34.12 |
Mean | 93.53 ± 10.36 | 84.11 ± 28.24 | 68.64 ± 37.03 | 99.35 ± 1.23 | 68.80 ± 35.79 | 62.77 ± 34.16 | 70.10 ± 34.05 |
Eigenloss | 93.50 ± 10.60 | 83.31 ± 28.49 | 69.52 ± 36.50 | 99.28 ± 1.76 | 69.51 ± 35.31 | 63.24 ± 33.86 | 70.61 ± 33.68 |
Norm Eigenloss | 93.12 ± 10.59 | 84.35 ± 28.65 | 65.74 ± 37.70 | 99.33 ± 1.61 | 66.13 ± 36.53 | 60.62 ± 35.21 | 67.93 ± 35.03 |
Loss Function | Accuracy | Precision | Recall | Specificity | F2-Score | Jaccard | Dice |
---|---|---|---|---|---|---|---|
Jaccard | 94.71 ± 9.08 | 87.32 ± 20.08 | 81.92 ± 27.25 | 98.58 ± 3.05 | 79.84 ± 26.01 | 71.57 ± 28.02 | 79.44 ± 24.79 |
Dice | 94.47 ± 9.90 | 86.44 ± 23.22 | 76.91 ± 34.45 | 99.19 ± 1.52 | 76.27 ± 33.16 | 69.71 ± 32.18 | 76.32 ± 31.22 |
Binary entropy | 93.99 ± 10.18 | 84.52 ± 25.45 | 77.44 ± 34.21 | 98.70 ± 2.73 | 75.96 ± 32.98 | 68.74 ± 32.73 | 75.44 ± 31.57 |
Binary focal | 94.39 ± 9.88 | 85.15 ± 25.81 | 77.04 ± 34.42 | 99.08 ± 2.07 | 75.99 ± 33.15 | 69.11 ± 32.62 | 75.71 ± 31.76 |
Tversky | 94.68 ± 9.41 | 88.13 ± 21.48 | 78.47 ± 31.38 | 99.00 ± 2.37 | 77.74 ± 29.93 | 70.92 ± 30.11 | 78.12 ± 28.08 |
Focal Tversky | 94.31 ± 9.64 | 85.96 ± 21.48 | 81.01 ± 29.48 | 98.52 ± 3.11 | 79.16 ± 27.99 | 70.84 ± 28.83 | 78.53 ± 26.54 |
Lovász-Hinge | 94.89 ± 9.56 | 86.73 ± 22.64 | 79.62 ± 31.09 | 99.21 ± 1.29 | 79.02 ± 30.08 | 72.41 ± 29.88 | 79.13 ± 28.59 |
Sum | 94.36 ± 9.65 | 84.13 ± 25.73 | 76.50 ± 34.19 | 99.01 ± 1.67 | 75.47 ± 32.69 | 68.36 ± 32.11 | 75.45 ± 30.70 |
Mean | 94.11 ± 10.09 | 85.48 ± 26.24 | 74.92 ± 34.48 | 98.96 ± 2.67 | 74.17 ± 33.11 | 67.77 ± 32.82 | 74.7 ± 31.61 |
Eigenloss | 94.59 ± 9.61 | 88.65 ± 18.25 | 80.23 ± 30.78 | 99.05 ± 1.92 | 79.18 ± 29.26 | 71.84 ± 29.25 | 79.07 ± 27.07 |
NormEigenloss | 94.60 ± 9.86 | 87.79 ± 21.65 | 77.81 ± 32.85 | 99.16 ± 1.89 | 77.49 ± 31.66 | 71.57 ± 31.28 | 78.06 ± 29.85 |
U-Net-VGG-16 | U-Net-Densenet121 | LinkNet-VGG-16 | LinkNet-Densenet121 | |||||
---|---|---|---|---|---|---|---|---|
Eigenvalue | 5.803 | 6.076 | 5.913 | 5.998 | ||||
% Variance | 82.91 | 86.80 | 84.47 | 85.69 | ||||
Loss function | Com. | Com. | Com. | Com. | ||||
Jaccard | 0.875 | 0.935 | 0.819 | 0.905 | 0.843 | 0.918 | 0.819 | 0.905 |
Dice | 0.845 | 0.919 | 0.894 | 0.946 | 0.864 | 0.930 | 0.878 | 0.937 |
Binary cross entropy | 0.835 | 0.914 | 0.785 | 0.886 | 0.866 | 0.930 | 0.842 | 0.918 |
Binary focal | 0.826 | 0.909 | 0.892 | 0.944 | 0.805 | 0.897 | 0.861 | 0.928 |
Tversky | 0.881 | 0.939 | 0.906 | 0.952 | 0.842 | 0.917 | 0.881 | 0.939 |
Focal Tversky | 0.884 | 0.940 | 0.869 | 0.932 | 0.888 | 0.942 | 0.878 | 0.937 |
Lovász-Hinge | 0.657 | 0.811 | 0.911 | 0.954 | 0.805 | 0.897 | 0.840 | 0.916 |
U-Net-VGG-16 | U-Net-Densenet121 | LinkNet-VGG-16 | LinkNet-Densenet121 | |||||
---|---|---|---|---|---|---|---|---|
Loss Function | # Epoch | Val Loss | # Epoch | Val Loss | # Epoch | Val Loss | # Epoch | Val Loss |
Jaccard | 292 | 0.17 | 71 | 0.16 | 292 | 0.18 | 17 | 0.15 |
Dice | 216 | 0.19 | 71 | 0.12 | 211 | 0.17 | 71 | 0.16 |
Binary cross entropy | 292 | 0.16 | 71 | 0.15 | 292 | 0.15 | 211 | 0.14 |
Binary focal | 198 | 0.20 | 153 | 0.19 | 292 | 0.15 | 17 | 0.16 |
Tversky | 292 | 0.16 | 71 | 0.16 | 292 | 0.18 | 47 | 0.18 |
Focal Tversky | 216 | 0.20 | 71 | 0.14 | 292 | 0.16 | 211 | 0.17 |
Lovász-Hinge | 184 | 0.19 | 71 | 0.15 | 216 | 0.16 | 17 | 0.16 |
Sum | 259 | 0.22 | 71 | 0.15 | 292 | 0.18 | 128 | 0.15 |
Mean | 172 | 0.16 | 71 | 0.14 | 211 | 0.12 | 17 | 0.14 |
Eigenloss | 211 | 0.19 | 114 | 0.17 | 211 | 0.14 | 17 | 0.16 |
Normalized eigenloss | 211 | 0.20 | 71 | 0.13 | 292 | 0.16 | 17 | 0.18 |
Work | Accuracy | Precision | Recall | Specificity | F2-Score | Jaccard | Dice |
---|---|---|---|---|---|---|---|
U-Net-VGG-16 | 93.00 ± 10.91 | 80.31 ± 31.35 | 65.07 ± 37.70 | 99.00 ± 2.25 | 65.45 ± 36.70 | 59.66 ± 35.32 | 67.04 ± 35.39 |
U-Net-Densenet121 | 94.43 ± 9.78 | 89.83 ± 21.63 | 77.39 ± 33.28 | 99.06 ± 2.18 | 76.41 ± 31.97 | 69.35 ± 31.55 | 76.33 ± 30.39 |
LinkNet-VGG-16 | 93.50 ± 10.60 | 83.31 ± 28.49 | 69.52 ± 36.50 | 99.28 ± 1.76 | 69.51 ± 35.31 | 63.24 ± 33.86 | 70.61 ± 33.68 |
LinkNet-Densenet121 | 94.59 ± 9.61 | 88.65 ± 18.25 | 80.23 ± 30.78 | 99.05 ± 1.92 | 79.18 ± 29.26 | 71.84 ± 29.25 | 79.07 ± 27.07 |
Vázquez et al. [39] | 96.77 | - | - | - | - | 56.07 | - |
Wichakam et al. [37] | - | 88.84 | 78.14 | - | - | 69.36 | 78.61 |
Wickstrøm et al. [41] | 94.90 | - | - | - | - | 58.70 | - |
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Share and Cite
Sánchez-Peralta, L.F.; Picón, A.; Antequera-Barroso, J.A.; Ortega-Morán, J.F.; Sánchez-Margallo, F.M.; Pagador, J.B. Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation. Mathematics 2020, 8, 1316. https://doi.org/10.3390/math8081316
Sánchez-Peralta LF, Picón A, Antequera-Barroso JA, Ortega-Morán JF, Sánchez-Margallo FM, Pagador JB. Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation. Mathematics. 2020; 8(8):1316. https://doi.org/10.3390/math8081316
Chicago/Turabian StyleSánchez-Peralta, Luisa F., Artzai Picón, Juan Antonio Antequera-Barroso, Juan Francisco Ortega-Morán, Francisco M. Sánchez-Margallo, and J. Blas Pagador. 2020. "Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation" Mathematics 8, no. 8: 1316. https://doi.org/10.3390/math8081316