Table 3 The comparison between our proposed model and other algorithms on the internal testing set
Model | GTV segmentation | EGFR genotyping | ||||||
|---|---|---|---|---|---|---|---|---|
Dice | \(HD_{95}\) (mm) | Precision | Recall | Accuracy | Precision | Recall | F1-score | |
RA-Uent [43] | 0.8729 (0.85, 0.90) | 3.67 | 0.9128 (0.90, 0.93) | 0.8697 (0.85, 0.89) | – | – | – | – |
Swin Unet [40] | 0.6395 (0.61, 0.67) | 6.51 | 0.7143 (0.69, 0.74) | 0.6574 (0.62, 0.69) | – | – | – | – |
TransUnet [41] | 0.8565 (0.84, 0.88) | 3.78 | 0.8794 (0.84, 0.89) | 0.8631 (0.85, 0.88) | – | – | – | – |
Unet [38] | 0.8888 (0.86, 0.90) | 3.63 | 0.9031 (0.87, 0.91) | 0.9011 (0.89, 0.92) | – | – | – | – |
DeSeg [23] | 0.8566 (0.83, 0.87) | 4.13 | 0.8890 (0.86, 0.91) | 0.8789 (0.85, 0.89) | – | – | – | – |
Unet3+ [34] | 0.7946 (0.77, 0.82) | 4.63 | 0.7205 (0.70, 0.74) | 0.9412 (0.92, 0.96) | – | – | – | – |
ResNet-50 [16] | – | – | – | – | 0.7879 (0.65, 0.93) | 0.8333 (0.71, 0.96) | 0.7895 (0.65, 0.93) | 0.8108 |
Radiomics Model [14] | – | – | – | – | 0.6061 (0.44, 0.77) | 0.6667 (0.51, 0.83) | 0.6316 (0.47, 0.80) | 0.6486 |
RN-GAP [35] | – | – | – | – | 0.7273 (0.58, 0.88) | 0.7778 (0.64, 0.92) | 0.7368 (0.59, 0.89) | 0.7568 |
SE-Net [37] | – | – | – | – | 0.8182 (0.69, 0.95) | 0.8824 (0.77, 0.99) | 0.7895 (0.65, 0.93) | 0.8333 |
DenseNet [36] | – | – | – | – | 0.7879 (0.65, 0.93) | 0.8000 (0.66, 0.94) | 0.8421 (0.72, 0.97) | 0.8205 |
MTSA-Net (ours) | 0.8914 (0.88, 0.91) | 3.58 | 0.9063 (0.88, 0.92) | 0.9035 (0.89, 0.92) | 0.8788 (0.77, 0.99) | 0.9412 (0.86, 1.00) | 0.8421 (0.72, 0.97) | 0.8889 |