From Redaction to Restoration: Deep Learning for Medical Image Deidentification and Reconstruction

Vollständiger Abstract

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Abstract Removing patient-identifying information from medical images is a prerequisite for sharing image data directly, as in public dataset release and open benchmarks, where the images themselves, rather than only model updates must leave the originating institution. However, many methods currently used for de-identification, e.g., cropping or blacking out image regions to eliminate burned-in text, can have negative effects on downstream image analysis tasks because of removal of relevant but non-identifiable information. This work presents an end-to-end deep learning framework for transforming raw clinical image volumes into de-identified, analysis-ready datasets without compromising downstream utility. The methodology developed and tested in this work first detects and redacts regions likely to contain protected health information (PHI), such as burned-in text and metadata, and then uses a generative deep learning model to inpaint the redacted areas with anatomically and imaging-plausible content. The proposed pipeline leverages a lightweight hybrid architecture, combining CRNN-based redaction with a latent-diffusion inpainting restoration module (Stable Diffusion 2). We evaluate the approach using both privacy-oriented metrics, which quantify residual PHI and success of redaction, and image-quality and task-based metrics, which assess the fidelity of restored volumes for representative deep learning applications. The binary mask performance shows strong overall PHI identification (F1 score = 0.891 ± 0.037, recall of 0.912 ± 0.053, and precision of 0.875 ± 0.058, indicating accurate localization of PHI-containing regions with few missed detections or false-positive redactions. Downstream anatomy identification (segmentation) tasks remain markedly similar across after applying varied inpainting strategies (Diffusion with/without context and Telea with Dice ranging from 0.936 to 0.948 on one dataset and 0.955 to 0.959 on another. Our results suggest that the proposed method yields de-identified medical images that are visually coherent, maintaining fidelity for downstream models and clinical tasks, while substantially reducing the risk of patient re-identification. By automating de-identification and image reconstruction within a single workflow and disseminating large-scale medical imaging collections, thereby lowering a key barrier to data sharing and multi-institutional collaboration in medical imaging AI.