Semi-supervised few-shot learning for medical image segmentation

Understand

Recent years have witnessed the great progress of deep neural networks on semantic segmentation, particularly in medical imaging.

• Nevertheless, training high-performing models require large amounts of pixel-level ground truth masks, which can be prohibitive to obtain in the medical domain.
• Furthermore, training such models in a low-data regime highly increases the risk of overfitting.
• Recent attempts to alleviate the need for large annotated datasets have developed training strategies under the few-shot learning paradigm, which addresses this shortcoming by learning a novel class from only a few labeled examples.