{
  "annotation_id": "7e01e91c-98b9-4c33-af87-f3396e2efe0d",
  "date_created": "2026-02-17T05:53:15.903000Z",
  "date_modified": "2026-02-17T05:53:15.903000Z",
  "file_hash": "b88d5350239f05fb69062a2c8743e8a3f2a87f4abf06b384f3f7419d5d3f1631",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.08604",
    "authors": [
      "Yaxi Chen",
      "Simin Ni",
      "Shuai Li",
      "Shaheer U. Saeed",
      "Aleksandra Ivanova",
      "Rikin Hargunani",
      "Jie Huang",
      "Chaozong Liu",
      "Yipeng Hu"
    ],
    "categories": [
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Interpretability and Individuality in Knee MRI: Patient-Specific Radiomic Fingerprint with Reconstructed Healthy Personas",
    "url": "https://arxiv.org/abs/2601.08604",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "f2959bcd-4272-403b-9abd-e3f746c37f87",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}