{"ID":2841358,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12256","arxiv_id":"2511.12256","title":"Prompt-Conditioned FiLM and Multi-Scale Fusion on MedSigLIP for Low-Dose CT Quality Assessment","abstract":"We propose a prompt-conditioned framework built on MedSigLIP that injects textual priors via Feature-wise Linear Modulation (FiLM) and multi-scale pooling. Text prompts condition patch-token features on clinical intent, enabling data-efficient learning and rapid adaptation. The architecture combines global, local, and texture-aware pooling through separate regression heads fused by a lightweight MLP, trained with pairwise ranking loss. Evaluated on the LDCTIQA2023 (a public LDCT quality assessment challenge) with 1,000 training images, we achieve PLCC = 0.9575, SROCC = 0.9561, and KROCC = 0.8301, surpassing the top-ranked published challenge submissions and demonstrating the effectiveness of our prompt-guided approach.","short_abstract":"We propose a prompt-conditioned framework built on MedSigLIP that injects textual priors via Feature-wise Linear Modulation (FiLM) and multi-scale pooling. Text prompts condition patch-token features on clinical intent, enabling data-efficient learning and rapid adaptation. The architecture combines global, local, and...","url_abs":"https://arxiv.org/abs/2511.12256","url_pdf":"https://arxiv.org/pdf/2511.12256v1","authors":"[\"Tolga Demiroglu\",\"Mehmet Ozan Unal\",\"Metin Ertas\",\"Isa Yildirim\"]","published":"2025-11-15T15:26:59Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"eess.IV\"]","methods":"[]","has_code":false}