Multimodal Representation Distribution Learning for Medical Image Segmentation
Authors: Chao Huang, Weichao Cai, Qiuping Jiang, Zhihua Wang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three datasets show that our method has superior performance. |
| Researcher Affiliation | Academia | Chao Huang1 , Weichao Cai1 , Qiuping Jiang2 and Zhihua Wang3 1School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University 2School of Information Science and Engineering, Ningbo University 3Department of Engineering, Shenzhen MSU-BIT University |
| Pseudocode | No | The paper describes its methodology using architectural descriptions and mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source codes will be available at https://github.com/GPIOX/Multimodal.git. |
| Open Datasets | Yes | The proposed method is evaluated on three medical image segmentation datasets: Mo Nu Seg [Kumar et al., 2017], Mos Med Data+ [Li et al., 2023b], and Gla S [Sirinukunwattana et al., 2017]. |
| Dataset Splits | Yes | The ratio of training, validation, and test sets are the same as in [Li et al., 2023b]. |
| Hardware Specification | Yes | All experiments are conducted on a single NVIDIA RTX 3090 GPU with 24GB memory. |
| Software Dependencies | No | The paper mentions optimizers like Adam W and schedulers but does not specify software versions for programming languages or libraries (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The initial learning rate is set to 1e-3 for all datasets. Image input sizes are 224 × 224 both for Mo Nu Seg, Gla S, and Mos Med Data+. An early stop mechanism is adopted until the performance of the model does not increase for 50 epochs. The batch size is 2 for Mo Nu Seg and Gla S and 24 for Mos Med Data+. The default number of learnable features K is set to 32. Base on experiments, we set λ1 = 0.5, λ2 = 0.5, and λ3 = 1. |