Scaling Supervision for Free: Leveraging Universal Segmentation Models for Enhanced Medical Image Diagnosis

Yingtai Li, Shuai Ming, Haoran Lai, Fenghe Tang, Wei Wei, S Kevin Zhou

Keywords: medical image analysis, automated annotation, supervision scaling

Abstract:

Deep learning-based medical image analysis has been constrained by the limited availability of large-scale annotated data. While recent advances in large language models have enabled scaling automatic extraction of diagnostic labels from reports, we propose that scaling other form of supervision could be an equally important yet unexplored direction. Inspired by the success of foundation models, we leverage modern universal segmentation model to scale anatomical segmentation as an additional supervision signal during training.
Through extensive experiments on three large-scale CT datasets totaling 58K+ volumes, we demonstrate that incorporating this ``free" anatomical supervision consistently improves the performance of various mainstream architectures (ResNet, ViT, and Swin Transformer) by up to 12.74\%, with particularly significant gains for Transformer-based models and anatomically-localized abnormalities, while maintaining inference efficiency as the segmentation branch is only used during training. This work opens up new direction for scaling in medical imaging and demonstrates how existing universal segmentation models can be repurposed to enhance diagnostic models at virtually no additional cost.

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Application: Radiology

Paper Type: Methodological Development

Registration Requirement: Yes

Visa & Travel: Yes

Midl Latex Submission Checklist: Ensure no LaTeX errors during compilation., Created a single midl25_NNN.zip file with midl25_NNN.tex, midl25_NNN.bib, all necessary figures and files., Includes \documentclass{midl}, \jmlryear{2025}, \jmlrworkshop, \jmlrvolume, \editors, and correct \bibliography command., Did not override options of the hyperref package, Did not use the times package., All authors and co-authors are correctly listed with proper spelling and avoid Unicode characters., Author and institution details are de-anonymized where needed. All author names, affiliations, and paper title are correctly spelled and capitalized in the biography section., References must use the .bib file. Did not override the bibliographystyle defined in midl.cls. Did not use \begin{thebibliography} directly to insert references., Tables and figures do not overflow margins; avoid using \scalebox; used \resizebox when needed., Included all necessary figures and removed *unused* files in the zip archive., Removed special formatting, visual annotations, and highlights used during rebuttal., All special characters in the paper and .bib file use LaTeX commands (e.g., \'e for é)., Appendices and supplementary material are included in the same PDF after references., Main paper does not exceed 9 pages; acknowledgements, references, and appendix start on page 10 or later.

Submission Number: 165