Cross-Domain Semi-Supervised Organ Detection
MCML Authors
Morteza Ghahremani
Dr.
Benedikt Wiestler
Prof. Dr.
Principal Investigator
Christian Wachinger
Prof. Dr.
Principal Investigator
Abstract
Morteza Ghahremani
Dr.
Benedikt Wiestler
Prof. Dr.
Principal Investigator
Christian Wachinger
Prof. Dr.
Principal Investigator
Abstract
Accurate 3D organ detection in Computed Tomography (CT) imaging is crucial for various clinical applications. However, learning-based detection models rely on large, annotated datasets obtained from diverse imaging devices across multiple healthcare institutions, which are expensive and labor-intensive to acquire. Traditional semi-supervised approaches often rely solely on unlabeled target data, neglecting the benefits of a few labeled target samples. To address this limitation, we introduce a novel cross-domain semi-supervised detection framework (CDSS-Det) built upon the Transformer-based Organ-DETR model. CDSS-Det synergistically integrates pseudo-labeling, curriculum learning, and domain adaptation to enable effective knowledge transfer from a well-annotated source domain to a target domain with limited labels. Experiments on multi-domain CT datasets demonstrate that incorporating a small number of labeled target samples significantly boosts detection performance over conventional domain adaptation and semi-supervised methods. CDSS-Det consistently achieves higher mean Average Precision (mAP), with notable improvements in detecting small organs, and surpasses a fully supervised model trained solely on the labeled target domain by over 10%. These results underscore the potential of CDSS-Det in efficiently leveraging both labeled and unlabeled target data in cross-domain organ detection, advancing annotation-efficient deep learning models in medical imaging.
misc LGJ+25
Preprint
Nov. 2025Authors
N. Li • M. Ghahremani • B. Jian • P. T. Cervera • B. Wiestler • M. Makowski • C. WachingerLinks
URLResearch Area
BibTeXKey: LGJ+25