Domain-adaptive semi-supervised learning for efficient rare pathological lesion detection with minimal annotation
Isao Matsui, Ayumi Matsumoto, Atsuhiro Imai, Hiroki Okushima, Hirohiko Niioka, Masatoshi Abe, Natsune Tamai, Hajime Nagasu, Eiichiro Kanda, Eiichiro Uchino, Tadashi Sofue, Toshiyuki Imasawa, Yuichiro Yano, Hiroshi Kinashi, Ken‐ichi Miyoshi, Tamaki Harada, Yasuyuki Nagasawa, Keiji Fujimoto, Yuka Kurokawa, Sawako Kato · npj Digital Medicine · 2025
Artificial intelligence for rare pathological lesion detection faces dual challenges: expert annotation scarcity and domain shifts across institutions. Using multi-institutional kidney biopsies from 22 hospitals with 3 scanner types (NDPI, VSI, SVS), we demonstrate that model performance decreases dramatically across domains, with up to 70.3% reduction in detection precision for rare lesions such as crescents and segmental sclerosis (comprising only 2-3% of annotations). We present an approach integrating semi-supervised learning with residual CycleGAN-based domain adaptation, reducing mean Fréchet inception distance between institutions from 55.9 to 20.2 while preserving diagnostic morphology. We identified context-dependent optimal strategies: semi-supervised learning with 50% confidence threshold excelled in same-hospital scenarios (15.2-17.7% improvement for rare lesions), while our combined GAN-Semi-Supervised approach demonstrated superior performance in cross-scanner scenarios between NDPI and VSI formats (up to 63.4% improvement for crescents). This methodology enables robust performance across diverse healthcare settings with minimal expert annotation.