Sign inGet started
Paper · arXiv

Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation

Deploying high-performing 3D medical image segmenters (e.g., nnU-Net) is often limited by memory footprint and inference latency. Compression is therefore necessary, but compact 3D encoders tend to lose fine structural cues (small lesions and sharp boundaries) as downsampling repeats across multi-resolution stages. We propose Detail Consistent Distillation (DCD), a stage-wise distillation framework that preserves structural detail across scales by aligning teacher-student features in a wavelet-decomposed representation. At each encoder stage, DCD distills directional detail components in the wavelet domain while leaving the coarse approximation comparatively unconstrained, avoiding over-r...

AuthorsMengchen Fan, Baocheng Geng, Xi Xiao et al.
Primary categorycs.CV
Published25 May 2026
Last updated at source25 May 2026
cs.CV
Embed this entry
<iframe src="https://silytics.si/embed/item/ea68f6ff-e221-406f-a3d0-9857766d599d" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.