Benchmarking and fine-tuning vision-language models on a visual question answering dataset for myopic maculopathy.
Yip; Xu; Liu; Zaabaar; Fong; Guo; Xu; Zhang · 2026 · Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
To build a visual question answering (VQA) dataset for fine-tuning and evaluating vision-language models (VLMs) in myopic maculopathy (MM). Cross-sectional study. Colour fundus photographs (CFPs) from two publicly available datasets were…
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Metadata source: PubMed · DOI 10.1016/j.apjo.2026.100345