  Active learning selects the most informative samples from the unlabelled
dataset to annotate in the context of a limited annotation budget. While
numerous methods have been proposed for subsequent sample selection based on an
initialized model, scant attention has been paid to the indispensable phase of
active learning: selecting samples for model cold-start initialization. Most of
the previous studies resort to random sampling or naive clustering. However,
random sampling is prone to fluctuation, and naive clustering suffers from
convergence speed, particularly when dealing with high-dimensional data such as
imaging data. In this work, we propose to integrate foundation models with
clustering methods to select samples for cold-start active learning
initialization. Foundation models refer to those trained on massive datasets by
the self-supervised paradigm and capable of generating informative and
compacted embeddings for various downstream tasks. Leveraging these embeddings
to replace raw features such as pixel values, clustering quickly converges and
identifies better initial samples. For a comprehensive comparison, we included
a classic ImageNet-supervised model to acquire embeddings. Experiments on two
clinical tasks of image classification and segmentation demonstrated that
foundation model-based clustering efficiently pinpointed informative initial
samples, leading to models showcasing enhanced performance than the baseline
methods. We envisage that this study provides an effective paradigm for future
cold-start active learning.
