![]() Specifically, we construct the training set using not only the images form the source class, but also the support set from the target class by resampling the latter for regularizing the training. Then, to solve the domain shift problem, we propose a resampling strategy to regularize the model training. To tackle these challenges, we adapt the prototypical networks from the few-shot learning scheme as our base model architecture to learn a compact feature representation of the object, with only a few annotated masks. These methods, however, often fail to generalize to FMIs due to the domain shift from the images of source class to those of target class. Few-shot segmentation (FSS) models reduce the requirements of annotations by using prototypical networks to generalize the object regions given only a few annotation masks. These issues result in time-consuming manual annotations for training a segmentation model. The objects in fluorescence microscopy images (FMIs) are rather densely populated, and often have complex structures as well as large color variations.
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