Abstract:
The field of ophthalmic biomarker identification plays a pivotal role in understanding and
monitoring eye health. In this study, I leverage the EfficientViT_m5.r224_inlk model as
our foundational framework to explore constructive learning approaches for enhancing
biomarker identification accuracy. Initially, the model achieved a baseline accuracy of
69%. However, through the integration of contrastive learning techniques, a significant
improvement, achieving an accuracy of 73%.The contrastive learning is used on multilabel classes of images with different approaches.I introduce a nobel contrastive learning
on label and unlabeled data for pre-train a model in this study.This research delves into the
methodologies of constructive learning, shedding light on how these approaches contribute
to the identification of key biomarkers related to eye health. The incorporation of
contrastive learning has proven to be particularly effective, unveiling insights that go
beyond the capabilities of traditional models. The findings underscore the importance of
leveraging advanced learning techniques in ophthalmic biomarker identification, providing
a more nuanced understanding of eye health. As precision in biomarker identification is
crucial for early detection and intervention in ocular diseases, my study contributes to the
ongoing efforts aimed at improving diagnostic capabilities in the realm of ophthalmology
and applying contrastive learning on multi-label classes with different from traditional
approaches.The successful application of contrastive learning not only enhances accuracy
but also opens avenues for further exploration and refinement of ophthalmic biomarker
identification methodologies.
