Belangrijkste concepten
The author proposes a novel probabilistic-aware weakly supervised learning pipeline for 3D medical imaging, integrating innovative components to enhance segmentation accuracy with minimal annotations.
Samenvatting
The content discusses a novel approach to 3D medical image segmentation using weak supervision and probabilistic modeling. It introduces a unique pipeline that outperforms existing methods, showcasing significant improvements in accuracy and efficiency.
Recent advances in deep learning have significantly impacted fully supervised medical image segmentation. However, the reliance on labor-intensive annotations remains a challenge, prompting the development of weakly supervised methods. The proposed probabilistic-aware pipeline integrates innovative components like Probability-based Pseudo Label Generation and Probabilistic Multi-head Self-Attention network to enhance training efficiency with minimal annotation costs.
The method demonstrates substantial advancements over fully supervised and existing weakly supervised approaches in CT and MRI datasets. By leveraging probability integration throughout training and inference, the approach achieves remarkable improvements in Dice scores for various organs. The study highlights the potential of this method as a robust solution for efficient medical image segmentation under weak supervision.
Statistieken
Achieving up to an 18.1% improvement in Dice scores for certain organs.
Demonstrates enhancements of 58.4% and 17.6% over scribble-supervised methods.
Achieved results similar to or surpassing one of the fully supervised tests.