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Jan 1, 2018 · This paper introduces an innovative approach for supervised learning systems in cases when we do not have initially defined training data sets, but we need to develop them gradually during ...
Jun 10, 2021 · Domain adaptation to ED3, ED2 and ED1 using MD-nets was achieved in an unsupervised manner. The networks were initially tested with a hold-out test set of 742 ED4 embryo images, followed by tests...
- Manoj Kumar Kanakasabapathy, Prudhvi Thirumalaraju, Hemanth Kandula, Fenil Doshi, Anjali Devi Sivaku...
- 2021
Nov 15, 2019 · Semi-supervised learning is a branch of machine learning that aims to combine these two tasks (Chapelle et al. 2006b; Zhu 2008 ). Typically, semi-supervised learning algorithms attempt to improve performance in one of these two tasks by utilizing information generally associated with the other.
- Jesper E. van Engelen, Holger H. Hoos, Holger H. Hoos
- 2020
Mar 4, 2019 · In this work, we propose a semi-supervised learning (SSL) approach to brain lesion segmentation, where unannotated data is incorporated into the training of CNNs. We adapt the mean teacher model, which is originally developed for SSL-based image classification, for brain lesion segmentation.
- Wenhui Cui, Yanlin Liu, Yuxing Li, Menghao Guo, Yiming Li, Xiuli Li, Tianle Wang, Xiangzhu Zeng, Chu...
- arXiv:1903.01248 [cs.CV]
- 2019
May 22, 2019 · The original MT model is developed for semi-supervised image classification, and its consistency loss is simply the difference between class predictions. In our task, however, the consistency needs to be enforced for segmentation. Thus, we have adapted the MT model by defining the consistency loss based on the Dice coefficient.
- Wenhui Cui, Yanlin Liu, Yuxing Li, Menghao Guo, Yiming Li, Xiuli Li, Tianle Wang, Xiangzhu Zeng, Chu...
- 2019
Abstract: As an effective solution to alleviating the insufficiency of labeled data for indoor positioning, deep semi-supervised learning (DSSL) can be employed to lessen the dependency on labeled data by exploiting potential patterns in unlabeled samples.
Oct 8, 2023 · Domain Knowledge Adapted Semi-supervised Learning with Mean-Teacher Strategy for Circulating Abnormal Cells Identification. Conference paper; First Online: 08 October 2023; pp 61–70; Cite this conference paper
