Research field (6)：
Soft computing
, Intelligent informatics
, Statistical science
, Intelligent robotics
, Perceptual information processing
, Space and planetary science
Research keywords (7)：
Medical Imaging
, Sparse modeling
, Signal processing
, Compressed sensing
, Computer vision
, Machine learning
, Pattern recognition
Research theme for competitive and other funds (4)：
2019 - 2023 Transfer learning from mathematical models and its applications in biomedical engineering
2014 - 2015 圧縮センシングに基づく超高次元非線形写像の機械学習に関する研究
2013 - 2015 Pattern recognition as combinatorial optimization: basic and applied research
2010 - 2013 Research on designing efficient, robust and ciphered patternrecognition schemes using compressed sensing techniques
Papers (106)：
Keita Takeda, Tomoya Sakai, Eiji Mitate. Background removal for debiasing computer-aided cytological diagnosis. International Journal of Computer Assisted Radiology and Surgery. 2024
Keita Takeda, Tomoya Sakai. Unsupervised deep learning of foreground objects from low-rank and sparse dataset. Computer Vision and Image Understanding. 2024. 240
Shunta Matsumoto, Keita Takeda, Tomoya Sakai, Eiji Mitate. Unsupervised cell detection for oral cytology and effects of feature reduction. The 5th Conference on Biological Imaging and Medical AI. 2023. JSMBE-BIMAI2022-03
Background subtraction approach to unsupervised cell segmentation: toward excluding spurious features in degraded cytology slides
(IEEE International Symposium on Biomedical Imaging 2023)
Unsupervised deep learning for online foreground segmentation exploiting low-rank and sparse priors
(2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA) 2022)
Unsupervised deep learning for foreground segmentation based on low-rank and sparse priors
(2022)
