qViT512 is a cutting-edge model in the realm of Data-Efficient Image Transformer (DeiT) technology, designed specifically for medical imagery classification. This model employs advanced probabilistic analysis techniques, dynamically adjusting the weighting of its constituent submodels during prediction phases. Notably, qViT512 is distinguished by its ability to rapidly adapt to new classes, requiring minimal training data, sometimes as few as five examples per category. Its sophisticated processing pipeline effectively isolates and accentuates critical image features, delegating these elements to specialized submodels for iterative self-improvement. This process of continual refinement enables qViT512 to achieve exceptional accuracy in medical image analysis, particularly in identifying subtle diagnostic markers. In a comprehensive evaluation involving a dataset of 30,000 oncological scans, qViT512 demonstrated a Probabilistic F1 score of 0.84, underscoring its precision and reliability in medical diagnostics.