Semi-supervised Medical Image Segmentation through Dual-task Consistency
Authors: Xiangde Luo, Jieneng Chen, Tao Song, Guotai Wang8801-8809
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two public datasets show that our method can largely improve the performance by incorporating the unlabeled data. Meanwhile, our framework outperforms the state-of-the-art semi-supervised learning methods. |
| Researcher Affiliation | Collaboration | Xiangde Luo1,2, Jieneng Chen3, Tao Song2, Guotai Wang1* 1University of Electronic Science and Technology of China, Chengdu, China 2 Sense Time Research, Shanghai, China 3Tongji University, Shanghai, China |
| Pseudocode | Yes | Algorithm 1 Semi-supervised training through Dual-task consistency |
| Open Source Code | Yes | Code is available at: https://github.com/Hi Lab-git/DTC |
| Open Datasets | Yes | To evaluate the proposed method, we apply our algorithm on two different datasets. The first is left atrial dataset (Xiong et al. 2020), which consists of 100 3D gadolinium-enhanced MR images... The second is pancreas dataset (Roth et al. 2015), which includes 82 abdomen CT images. |
| Dataset Splits | Yes | Following (Yu et al. 2019; Li, Zhang, and He 2020), we use 80 scans for training and 20 scans for validation, and apply the same pre-processing methods. The second is pancreas dataset (Roth et al. 2015), which includes 82 abdomen CT images. Following (Xia et al. 2020), we randomly split them into 62 images for training and 20 images for testing. |
| Hardware Specification | Yes | We implement our framework in Py Torch (Paszke et al. 2019), using an NVIDIA 1080TI GPU. |
| Software Dependencies | No | We implement our framework in Py Torch (Paszke et al. 2019), using an NVIDIA 1080TI GPU. |
| Experiment Setup | Yes | The framework is trained by an SGD optimizer for 6000 iterations, with an initial learning rate (lr) 0.01 decayed by 0.1 every 2500 iterations. The batch size is 4, consisting of 2 labeled images and 2 unlabeled images. Following (Xue et al. 2020), the value of k is set to 1500 in this work. We randomly crop 112 112 80 (3D MRI Left Atrium) and 96 96 96 (3D CT Pancreas) sub-volume as the network input. |