mlsanityData to 7 Oct 2026

RFS-UNet: Decoder-Conditioned High-Resolution Skip Recalibration for Bone-Selective DRR Synthesis

arXiv 2609.08044 ↗First posted 7 Sep 2026cs.CVcited by 0

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

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  4. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,618
  5. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  6. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 597
  7. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  8. 2024Merlin: A Computed Tomography Vision-Language Foundation Model and Datasetcited by 97
  9. 2018DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedurescited by 55
  10. 2022Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imagingcited by 37
  11. 2023Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentationcited by 9
  12. 2018Lung Structures Enhancement in Chest Radiographs via CT based FCNN Trainingcited by 5
3 more
  1. 2020Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Datacited by 5
  2. 2023Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomographycited by 3
  3. 2026ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selectioncited by 1

Built on by

Not cited yet.