In this paper, we propose a self-training method which uses unlabeled regions in the original images obtained from a colorectal Narrow Band Imaging (NBI) ...
In this paper, we propose a self-training method which uses unlabeled regions in the original images ob- tained from a colorectal Narrow Band Imaging (NBI).
In this paper, we propose a self-training method which uses unlabeled regions in the original images obtained from a colorectal Narrow Band Imaging (NBI) ...
Self-training with unlabeled regions for NBI image recognition. In Proceedings of the 21st International Conference on Pattern Recognition, ICPR 2012 ...
Self-training with unlabeled regions for NBI image recognition. ICPR 2012 ... A System for Colorectal Tumor Classification in Magnifying Endoscopic NBI Images.
Self-training with unlabeled regions for NBI image recognition ( TT , TT , BR , KK , TK , SY , YT , KO , RM , ST ), pp. 25–28.
Self-supervised learning can achieve excellent recognition performance with only a small amount of labeled data, providing a potential approach to solve the ...
Missing: NBI | Show results with:NBI
Self-training with unlabeled regions for NBI image recognition pp. 25-28 ... A comparison study on appearance-based object recognition pp. 3500-3503.
Feb 8, 2024 · In this study, we explored if SSL for pretraining on non-medical images can be applied to chest radiographs and how it compares to supervised pretraining.
Missing: NBI | Show results with:NBI
Self-training with unlabeled regions for NBI image recognitionTakahishi Takeda, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Takio Kurita, Shigeto Yoshida ...