Abstract
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.
Abstract (translated)
对于膝关节MRI扫描的自动化评估，准确性和可解释性对于临床使用和接受至关重要。传统放射组学依赖于在群体层面预先定义的特征选择；尽管更具可解释性，但它们往往过于严格，无法捕捉患者特异性变异，并且可能不如端到端深度学习（DL）表现良好。为了应对这一挑战，我们提出了两种互补策略，以引入个性和可解释性：放射组学指纹和健康人格。 首先，放射组学指纹是一种从MRI中动态构建的、特定于患者的特征集。与应用统一的群体层面签名不同，我们的模型会根据候选特征池预测特征的相关性，并仅选择对每位患者最具预测性的那些特征，同时保持了特征级别的可解释性。这种指纹可以视为一种特征使用的潜在变量模型，在该模型中，一个基于图像条件的预测器估计使用概率，而透明的逻辑回归则利用全局系数进行分类。 其次，健康人格为每个患者综合了一个无病理变化的基础线，这是通过训练扩散模型来重建健康膝关节MRI得到的结果。将病理性图像提取的特征与其相应的健康人格进行比较，可以突出偏离正常解剖结构的变化，并能够提供直观、特定病例的疾病表现解释。 我们系统地对比了指纹、健康人格及其组合在三项临床任务中的表现。实验结果显示，这两种方法都能获得与当前最先进的深度学习模型相当或更好的性能，同时支持多层面的可解释性。案例研究进一步说明了这些视角如何促进人类可以理解的生物标志物发现和病理定位。 这种方法不仅提高了膝关节MRI扫描自动评估的有效性和准确性，同时也增强了临床医生的理解能力和决策能力，从而推动了更个性化的医疗实践的发展。
URL
https://arxiv.org/abs/2601.08604
