RFS-UNet: Decoder-Conditioned High-Resolution Skip Recalibration for Bone-Selective DRR Synthesis
Not on the map. Why?
Abstract (arXiv)
Bone-selective synthesis from digitally reconstructed radiographs (DRRs) requires separating skeletal signal from overlying tissue while preserving anatomical detail. U-Net skip connections supply fine encoder features, but their transfer is independent of decoder context. We introduce RFS-UNet, which lets the decoder participate in high-resolution channel recalibration. Pooled encoder and decoder features jointly predict a bounded residual scale, initialized to preserve the original skip transfer. The module operates at the two finest resolutions and integrates directly into the backbone. On a rebuilt patient-unique cohort, RFS improves test PSNR over U-Net-64 by 0.10 dB. A three-seed comparison with encoder-only recalibration supports the contribution of decoder context. RFS also offers a lower-latency alternative to CBAM, with 2.25 times faster inference in matched profiling. Decoder-conditioned reuse thus improves bone-selective synthesis through a compact architectural change.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 7 Sep 2026.
Builds on
15 of 20 references.
References · 15
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2018CBAM: Convolutional Block Attention Modulecited by 2,463
- 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,618
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
- 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 597
- 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
- 2024Merlin: A Computed Tomography Vision-Language Foundation Model and Datasetcited by 97
- 2018DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedurescited by 55
- 2022Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imagingcited by 37
- 2023Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentationcited by 9
- 2018Lung Structures Enhancement in Chest Radiographs via CT based FCNN Trainingcited by 5
3 more
- 2020Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Datacited by 5
- 2023Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomographycited by 3
- 2026ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selectioncited by 1
Built on by
Not cited yet.