[2508.21435] MedShift: Implicit Conditional Transport for X-Ray Domain Adaptation

Computer Science > Computer Vision and Pattern Recognition arXiv:2508.21435 (cs) [Submitted on 29 Aug 2025 (v1), last revised 25 Mar 2026 (this version, v2)] Title:MedShift: Implicit Conditional Transport for X-Ray Domain Adaptation Authors:Francisco Caetano, Christiaan Viviers, Peter H.N. de With, Fons van der Sommen View a PDF of the paper titled MedShift: Implicit Conditional Transport for X-Ray Domain Adaptation, by Francisco Caetano and 3 other authors View PDF HTML (experimental) Abstract:Synthetic medical data offers a scalable solution for training robust models, but significant domain gaps limit its generalizability to real-world clinical settings. This paper addresses the challenge of cross-domain translation between synthetic and real X-ray images of the head, focusing on bridging discrepancies in attenuation behavior, noise characteristics, and soft tissue representation. We propose MedShift, a unified class-conditional generative model based on Flow Matching and Schrodinger Bridges, which enables high-fidelity, unpaired image translation across multiple domains. Unlike prior approaches that require domain-specific training or rely on paired data, MedShift learns a shared domain-agnostic latent space and supports seamless translation between any pair of domains seen during training. We introduce X-DigiSkull, a new dataset comprising aligned synthetic and real skull X-rays under varying radiation doses, to benchmark domain translation models. Experimental result...