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<p>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.</p>

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Domain-adaptive semi-supervised learning for efficient rare pathological lesion detection with minimal annotation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月23日

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  • 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,
  • Ryohei Kaseda,
  • Masahiro Koizumi,
  • Yasuo Kusunoki,
  • Masaki Ohya,
  • Yoshimasa Kawazoe,
  • Hiroyuki Abe,
  • Yuta Matsukuma,
  • Takaaki Kosugi,
  • Yoshiyasu Ueda,
  • Naohiko Fujii,
  • Masanobu Takeji,
  • Akira Suzuki,
  • Katsuyuki Nagatoya,
  • Kazumasa Oka,
  • Yutaka Ando,
  • Masaaki Izumi,
  • Toshiyuki Komiya,
  • Tatsuo Tsukamoto,
  • Imari Mimura,
  • Takahiro Kuragano,
  • Toshiaki Nakano,
  • Kazuhiko Tsuruya,
  • Yasuhiko Ito,
  • Tetsuo Minamino,
  • Osamu Yamaguchi,
  • Suguru Yamamoto,
  • Hirotaka Komaba,
  • Kengo Furuichi,
  • Kei Fukami,
  • Shin-ichi Araki,
  • Takao Masaki,
  • Naotake Tsuboi,
  • Hitoshi Yokoyama,
  • Akira Shimizu,
  • Tetsuo Ushiku,
  • Shoichi Maruyama,
  • Motoko Yanagita,
  • Masaomi Nangaku,
  • Ryohei Yamamoto,
  • Kazunori Inoue,
  • Yoshitaka Isaka

摘要

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.