Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography.

ABSTRACT: Limited availability of medical imaging datasets is a vital limitation when using "data hungry" deep learning to gain performance improvements. Dealing with the issue, transfer learning has become a de facto standard, where a pre-trained convolution neural network (CNN), typically on natural images (e.g., ImageNet), is finetuned on medical images. Meanwhile, pre-trained transformers, which are self-attention-based models, have become de facto standard in natural language processing (NLP) and state of the art in image classification due to their powerful transfer learning abilities. Inspired by the success of transformers in NLP and image classification, large-scale transformers (such as vision transformer) are trained on natural images. Based on these recent developments, this research aim

SUBMITTER: Usman M 

PROVIDER: S-EPMC9274969 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature