Rethinking Domain‐Specific Pretraining by Supervised or Self‐Supervised Learning for Chest Radiograph Classification: A Comparative Study Against ImageNet Counterparts in Cold‐Start Active Learning

In this study, we highlighted the limitations of medical pretraining compared to general pretraining in the context of cold-start AL. We also identified promising outcomes related to cold-start AL, including initialization based on pretrained models, the positive influence of initialization on subsequent learning, the potential for one-shot initialization, and the influence of class balance on middle-budget AL. Researchers are encouraged to improve medical pretraining for versatile DL foundations and explore novel AL methods.