Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images.

ABSTRACT: Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-wei

SUBMITTER: Rajaraman S 

PROVIDER: S-EPMC10793885 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature