Uni4Eye++: A General Masked Image Modeling Multi-modal Pre-training Framework for Ophthalmic Image Classification and Segmentation

A large-scale labeled dataset is a key factor for the success of supervised deep learning in most ophthalmic image analysis scenarios. However, limited annotated data is very common in ophthalmic image analysis, since manual annotation is time-consuming and labor-intensive. Self-supervised learning (SSL) methods bring huge opportunities for better utilizing unlabeled data, as they do not require massive annotations. To utilize as many unlabeled ophthalmic images as possible, it is necessary to break the dimension barrier, simultaneously making use of both 2D and 3D images as well as alleviating the issue of catastrophic forgetting. In this paper, we propose a universal self-supervised Transformer framework named
Uni4Eye++
to discover the intrinsic image characteristic and capture domain-specific feature embedding in ophthalmic images.
Uni4Eye++
can serve as a global feature extractor, which builds its basis on a Masked Image Modeling task with a Vision Transformer architecture. On the basis of our previous work
Uni4Eye
, we further employ an image entropy guided masking strategy to reconstruct more-informative patches and a dynamic head generator module to alleviate modality confusion. We evaluate the performance of our pre-trained
Uni4Eye++
encoder by fine-tuning it on multiple downstream ophthalmic image classification and segmentation tasks. The superiority of
Uni4Eye++
is successfully established through comparisons to other state-of-the-art SSL pre-training methods. Our code is available at Github
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