  Contemporary medical contrastive learning faces challenges from inconsistent
semantics and sample pair morphology, leading to dispersed and converging
semantic shifts. The variability in text reports, due to multiple authors,
complicates semantic consistency. To tackle these issues, we propose a two-step
approach. Initially, text reports are converted into a standardized triplet
format, laying the groundwork for our novel concept of ``observations'' and
``verdicts''. This approach refines the {Entity, Position, Exist} triplet into
binary questions, guiding towards a clear ``verdict''. We also innovate in
visual pre-training with a Meijering-based masking, focusing on features
representative of medical images' local context. By integrating this with our
text conversion method, our model advances cross-modal representation in a
multimodal contrastive learning framework, setting new benchmarks in medical
image analysis.
