Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation
Authors: Huifeng Yao, Xiaowei Hu, Xiaomeng Li3099-3107
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method sets new records on public datasets, i.e., M&Ms and SCGM. Notably, without using domain labels, our method surpasses the prior art that even uses domain labels by 11.67% on Dice on M&Ms dataset with 2% labeled data. |
| Researcher Affiliation | Academia | 1 Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology 2 Department of Computer Science and Engineering, The Chinese University of Hong Kong 3 The Hong Kong University of Science and Technology Shenzhen Research Institute |
| Pseudocode | No | The paper contains architectural diagrams (e.g., Figure 2, Figure 3) but does not include any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/XMed-Lab/EPL Semi DG. |
| Open Datasets | Yes | We adopt the multi-centre, multi-vendor & multi-disease cardiac image segmentation (M&Ms) dataset (Campello et al. 2021) to evaluate of our method. We also adopt the spinal cord gray matter segmentation (SCGM) dataset(Prados et al. 2017) to evaluate of our method. |
| Dataset Splits | No | The paper describes using a percentage of labeled data for training (e.g., '5% labeled data', '2% labeled data', '20% labeled data') and testing on an 'unseen domain' but does not provide explicit train/validation/test dataset splits by percentage or count that are typically needed to reproduce the data partitioning for evaluation. |
| Hardware Specification | Yes | We implemented the model on Pytorch1.8 and trained it by using two NVIDIA 3090 GPUs with 377GB RAM on the Ubuntu20.04 system. |
| Software Dependencies | Yes | We implemented the model on Pytorch1.8 |
| Experiment Setup | Yes | We leveraged Adam W to optimize the network with the weight decay of 0.1, the learning rate of 0.0001, and the batch size of 32. We trained the whole architecture for 20 epochs and the images were cropped to 288 288. We set β in equation 9 as three to balance the supervision loss and our proposed CACPS loss. We set λ in equation 1 as 1. For SCGM dataset, we leveraged Adam W to optimize the network with the weight decay of 0.1, the learning rate of 0.0001, and the batch size of eight. We trained the whole architecture for 50 epochs and the images were cropped to 288 288. We set β in equation 9 as 1.5 to balance the supervision loss and our proposed CACPS loss. We set λ in equation 1 as 0.8. We also adopt the random rotation, random scaling, random crop, and random flip as the data augmentation strategies. |