For automated assessment of knee MRI scans, both accuracy and interpretability are essential for clinical use and adoption. Traditional radiomics rely on predefined features chosen at the population level; while more interpretable, they are often too restrictive to capture patient-specific variability and can underperform end-to-end deep learning (DL). To address this, we propose two complementary strategies that bring individuality and interpretability: radiomic fingerprints and healthy personas. First, a radiomic fingerprint is a dynamically constructed, patient-specific feature set derived from MRI. Instead of applying a uniform population-level signature, our model predicts feature relevance from a pool of candidate features and selects only those most predictive for each patient, while maintaining feature-level interpretability. This fingerprint can be viewed as a latent-variable model of feature usage, where an image-conditioned predictor estimates usage probabilities and a transparent logistic regression with global coefficients performs classification. Second, a healthy persona synthesises a pathology-free baseline for each patient using a diffusion model trained to reconstruct healthy knee MRIs. Comparing features extracted from pathological images against their personas highlights deviations from normal anatomy, enabling intuitive, case-specific explanations of disease manifestations. We systematically compare fingerprints, personas, and their combination across three clinical tasks. Experimental results show that both approaches yield performance comparable to or surpassing state-of-the-art DL models, while supporting interpretability at multiple levels. Case studies further illustrate how these perspectives facilitate human-explainable biomarker discovery and pathology localisation.

翻译：在膝关节MRI扫描的自动化评估中，准确性和可解释性对于临床应用与采纳均至关重要。传统影像组学依赖于群体层面选择的预定义特征，虽然更具可解释性，但往往过于局限而无法捕捉患者特异性变异，且性能可能不及端到端深度学习模型。为解决这一问题，我们提出两种互补策略以引入个体化与可解释性：影像组学指纹与健康人格。首先，影像组学指纹是从MRI动态构建的患者特异性特征集。我们的模型并非采用统一的群体层面特征签名，而是从候选特征池中预测特征相关性，仅选择对每位患者最具预测性的特征，同时保持特征层面的可解释性。该指纹可视为特征使用的潜变量模型，其中图像条件预测器估计使用概率，而具有全局系数的透明逻辑回归执行分类。其次，健康人格通过训练用于重建健康膝关节MRI的扩散模型，为每位患者合成无病理的基线图像。通过比较从病理图像及其健康人格中提取的特征，可突出与正常解剖结构的偏差，从而实现对疾病表现的直观、病例特异性解释。我们在三项临床任务中系统比较了影像组学指纹、健康人格及其组合。实验结果表明，两种方法均取得与当前最先进深度学习模型相当或更优的性能，同时支持多层次可解释性。案例研究进一步阐释了这些视角如何促进人类可解释的生物标志物发现与病理定位。