Extremely imbalanced subarachnoid hemorrhage detection based on DenseNet-LSTM network with class-balanced loss and transfer learning

Zhongyang Lu, Masahiro Oda, Yuichiro Hayashi, Tao Hu, Hayato Itoh, Takeyuki Watadani, Osamu Abe, Masahiro Hashimoto, Masahiro Jinzaki, Kensaku Mori

研究成果: Conference contribution


Subarachnoid Hemorrhage (SAH) detection is a critical, severe problem that confused clinical residents for a long time. With the rise of deep learning technologies, SAH detection made a significant breakthrough in recent ten years. Whereas, the performances are significantly degraded on imbalanced data, makes deep learning models have always suffered criticism. In this study, we present a DenseNet-LSTM network with Class-Balanced Loss and the transfer learning strategy to solve the SAH detection problem on an extremely imbalanced dataset. Compared to the previous works, the proposed framework not merely effectively integrate greyscale features the and spatial information from the consecutive CT scans, but also employ Class-Balanced loss and transfer learning to alleviate the adverse effects and broaden feature diversity respectively on an extreme SAH cases scarcity dataset, mimicking the actual situation of emergency departments. Comprehensive experiments are conducted on a dataset, consisted of 2,519 cases without hemorrhage cases and only 33 cases with SAH. Experimental results demonstrate the F-measure score of SAH detection achieved a remarkable improvement, the backbone DenseNet121 gained around 33% promotion after transfer learning, and on this basis, importing the Class-Balanced Loss and the LSTM structure, the F-measure score further increased 6.1% and 2.7% sequentially.

ホスト出版物のタイトルMedical Imaging 2021
ホスト出版物のサブタイトルComputer-Aided Diagnosis
編集者Maciej A. Mazurowski, Karen Drukker
出版ステータスPublished - 2021
イベントMedical Imaging 2021: Computer-Aided Diagnosis - Virtual, Online, United States
継続期間: 2021 2月 152021 2月 19


名前Progress in Biomedical Optics and Imaging - Proceedings of SPIE


ConferenceMedical Imaging 2021: Computer-Aided Diagnosis
国/地域United States
CityVirtual, Online

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 生体材料
  • 原子分子物理学および光学
  • 放射線学、核医学およびイメージング


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