SAMed-2 is a new foundation model for medical image segmentation built upon the SAM-2 architecture. We introduce a temporal adapter into the image encoder to capture image correlations and a confidence-driven memory mechanism to store high-certainty features for later retrieval. This memory-based strategy counters the pervasive noise in large-scale medical datasets and mitigates catastrophic forgetting when encountering new tasks or modalities.
Figure 1: Workflow of SAMed-2. It integrates a temporal adapter in the image encoder to capture multi-dimensional context and a confidence-driven memory module to store high-certainty features. During inference, the model retrieves these memory features and fuses them with image embeddings via attention.
MedBank is a comprehensive collection of various public medical imaging datasets for training and evaluating medical image segmentation models. It covers diverse modalities and anatomical structures.
Datasets
Images
Organ Types
Modalities