ACL-Net: Semi-supervised Polyp Segmentation via Affinity Contrastive Learning
Authors: Huisi Wu, Wende Xie, Jingyin Lin, Xinrong Guo
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five benchmark datasets, including Kvasir-SEG, CVC-Clinic DB, CVC-300, Colon DB and ETIS, demonstrate the effectiveness and superiority of our method. |
| Researcher Affiliation | Academia | College of Computer Science and Software Engineering, Shenzhen University hswu@szu.edu.cn |
| Pseudocode | No | The paper describes the proposed method using textual explanations and mathematical equations (e.g., Equation 1 to 14) but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/xiewende/ACL-Net. |
| Open Datasets | Yes | Extensive experiments on five benchmark datasets, including Kvasir-SEG (Jha et al. 2020), CVC-Clinic DB (Bernal et al. 2015), CVC-300 (V azquez et al. 2017), Colon DB (Bernal, S anchez, and Vilarino 2012) and ETIS (Silva et al. 2014). |
| Dataset Splits | No | The paper states that 'a total of 1450 images... are divided into different labeled partition protocols (1/2, 1/4, 1/8) as our semi-supervised training datasets, and all above five datasets will be used in the inference phase'. While this describes training and inference, it does not explicitly specify a distinct validation dataset split or its size/proportion for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | Our proposed method is implemented with the Py Torch framework on a single NVDIA Ge Force RTX 3090TI. |
| Software Dependencies | No | The paper states 'Our proposed method is implemented with the Py Torch framework' but does not specify a version number for PyTorch or any other software dependencies like CUDA or specific library versions required for replication. |
| Experiment Setup | Yes | The initial learning rate is set to 0.001, while the batch size is set to 8. We used a stochastic gradient descent (SGD) optimizer for training with a weight decay of 0.0001. We unified all images resolution to 384 × 384. ... both the loss function weights λtra and λcon are experimentally set to 0.5. The weight of EMA is set to 0.999. We set the background scores βl = 0.45 and βh = 0.75 in Equation 2. The temperature parameter τ is 0.5 in Equation 4. In Equation 9 and Equation 10, we set the weight factors (w1, w2, w3) as (0.2, 0.2, 0.5) respectively. The weights (α1,α2) in Equation 11 are set to (0.3, 0.7). |