In response to the scarcity of annotated medical image data and the imitations of existing models in segmenting multi-scale target images, this paper proposes a few-shot medical image segmentation method based on multi-scale feature fusion and contrastive learning. First, a sequential concatenation-based multi-scale skip connection method is introduced to replace traditional skip connections, enabling effective fusion of multi-scale feature maps from the encoder and their transmission to the corresponding decoder. Second, considering the dual-branch structure of the model, a contrastive learning module based on multi-scale features is proposed, and a loss function is designed to enhance the model's discriminative ability at the pixel level. Experiments show that our method achieves cross-domain data segmentation for medical images, mitigates performance degradation due to dataset scarcity, and improves the segmentation accuracy and generalization for different-scale targets , outperforming current mainstream few-shot medical image segmentation methods.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |