Application of Deep Learning Methods to Processing of Noisy Medical Video Data
arXiv:2404.10319v1h-index: 6
Originality Synthesis-oriented
AI Analysis
This work addresses cell counting in medical imaging, which is incremental as it applies existing deep learning techniques with specific modifications.
The paper tackled the problem of counting cells in noisy medical video data where cells move continuously and have unclear boundaries, achieving improved accuracy through modifications in training and decision-making processes.
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.