Application of Deep Learning Methods to Processing of Noisy Medical Video Data
Not on the map. Why?
Abstract (arXiv)
Cells count become a challenging problem when the cells move in a continuous stream, and their boundaries are difficult for visual detection. To resolve this problem we modified the training and decision making processes using curriculum learning and multi-view predictions techniques, respectively.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 16 Apr 2024.
Builds on
13 of 24 references.
References · 13
- 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,053
- 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 733
- 2020X3D: Expanding Architectures for Efficient Video Recognitioncited by 635
- 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
- 2020Normalized Loss Functions for Deep Learning with Noisy Labelscited by 257
- 2020Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learningcited by 208
- 2019Competence-based Curriculum Learning for Neural Machine Translationcited by 201
- 2017Teacher-Student Curriculum Learningcited by 197
- 2020Hyperparameter Ensembles for Robustness and Uncertainty Quantificationcited by 149
- 2017Data Distillation: Towards Omni-Supervised Learningcited by 126
- 2021Sample Selection with Uncertainty of Losses for Learning with Noisy Labelscited by 79
- 2021To Smooth or Not? When Label Smoothing Meets Noisy Labelscited by 70
1 more
- 2021Understanding Softmax Confidence and Uncertaintycited by 62
Built on by
Not cited yet.