Adaptive patch sampling and location-aware reasoning for whole body PET-CT multi-organ segmentation
Junha Park1, Arthur Cho2, Hae-Jeong Park3,4,5,6,7,8
1Yonsei University College of Medicine, Seoul, South Korea.
Scientific Reports
|May 15, 2026
Summary
This study introduces adaptive patch sampling (APS) for neural network training, improving efficiency in dense prediction tasks by dynamically allocating computation. The adaptive patch sampling algorithm learns optimal sampling strategies based on model uncertainty and prediction error.
Area of Science:
- Medical Imaging
- Machine Learning
- Computer Vision
Background:
- Patch-wise learning is standard for dense prediction but assumes fixed sampling, which is inefficient when learning difficulty varies.
- Existing methods struggle with spatially varying and evolving learning difficulties during neural network optimization.
Purpose of the Study:
- To develop a dynamic computation allocation strategy for patch-wise learning.
- To improve the efficiency and performance of neural networks in large-scale dense prediction tasks.
Main Methods:
- Proposed an adaptive patch sampling (APS) algorithm that learns sampling distributions based on voxel-wise uncertainty and prediction error.
- Introduced a patch encoding (PE) block to infer implicit location information and modulate feature representations using context-dependent channel-wise attention.
- Conducted experiments on whole-body multi-organ PET-CT segmentation and validated on the Synapse dataset.
Main Results:
- Demonstrated faster convergence and consistent performance gains in PET-CT segmentation tasks.
- External validation on the Synapse dataset confirmed the robustness of the proposed methods.
- Mechanistic analyses revealed APS-induced sampling behaviors and PE-driven representation modulation.
Conclusions:
- The proposed adaptive patch sampling and patch encoding offer an efficient learning strategy for patch-wise training.
- Dynamic sampling and contextual conditioning significantly influence optimization in large-scale dense prediction tasks.
- This work provides valuable insights into optimizing deep learning models for medical image analysis.
