CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasets

Understand

Automated segmentation of ultrasound images can assist medical experts with diagnostic and therapeutic procedures.

• Although using the common modality of ultrasound, one typically needs separate datasets in order to segment, for example, different anatomical structures or lesions with different levels of malignancy.
• In this paper, we consider the problem of jointly learning from heterogeneous datasets so that the model can improve generalization abilities by leveraging the inherent variability among datasets.
• We merge the heterogeneous datasets into one dataset and refer to each component dataset as a subgroup.