用DINOv2-Small做MCI筛查，只训1.19M参数就比ResViT好，还自带可解释性，做医学影像分析的可以看看。
该论文提出一个基于冻结DINOv2-Small的参数高效框架，仅使用1.19M可训练参数（通过三个模态特定提示令牌），在共享跨注意力层中实现空间可解释性。引入MoCA自适应焦点损失，将连续认知评分整合到训练目标中，严格泛化标准软标签方法。在分层五折交叉验证中，MCI类F1达0.641，AUC达0.795，比计算量更大的ResViT基线提高0.110的F1。该方法克服了数据稀缺、类别不平衡和临床边界诊断模糊等限制。
Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening
Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundaries. Existing methodologies attempt to bypass these constraints using computationally expensive, fully fine-tuned hybrid architectures that relegate spatial explainability to a post-hoc approximation rather than an intrinsic model property. We propose a parameter-efficient framework utilizing frozen DINOv2-Small model adapted via three modality-specific learnable prompt tokens while Operating with 1.19 million trainable parameters, each token serves as a query in a shared cross-attention layer over the source image patch tokens. Crucially, spatial explainability is achieved directly through these attention maps; as a structural consequence of the architecture. Then task-conditioned embeddings fused via an attention module to quantify modality-level importance per subject. To handle boundary ambiguity, a MoCA-adapted focal loss introduced that integrates continuous cognitive scores into the training target, loss modulation, and adaptive sample weighting, strictly generalizing standard soft-label approaches. Under stratified five-fold cross-validation, the proposed architecture yields an MCI-class F1 of 0.641 and an AUC of 0.795, outperforming the computationally heavier ResViT baseline by 0.110 in MCI-class F1.