MCD-CLIP: Multi-view Chest Disease Diagnosis with Disentangled CLIP
Authors: Songyue Cai, Yujie Mo, Liang Peng, Yucheng Xie, Tao Tong, Xiaofeng Zhu
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the chest X-ray dataset demonstrate that MCDCLIP achieves comparable or better performance on a variety of tasks with 94.31% fewer tunable parameters compared to state-of-the-art methods. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, University of Electronic Science and Technology of China 2Department of Statistics and Actuarial Science, School of Computing and Data Science, The University of Hong Kong |
| Pseudocode | No | The paper describes the methodology through textual explanations and a flowchart in Figure 1, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source codes are released at https://github.com/ Yuzuno Kawori/MCD-CLIP. |
| Open Datasets | Yes | Dataset Che Xpert [Irvin et al., 2019] is a large publicly available chest X-ray image dataset. |
| Dataset Splits | No | The paper states: "We follow [Van Tulder et al., 2021; Black and Souvenir, 2024] to filter, pre-process, and divide the original dataset." However, it does not explicitly provide the specific percentages or counts for training, validation, and test splits within this paper. |
| Hardware Specification | Yes | All experiments are conducted with 2 NVIDIA Ge Force RTX-4090 GPUs. |
| Software Dependencies | No | The paper mentions using "CLIP pre-trained on Image Net" and "Vi T-B/32 as the image encoder" but does not specify any software versions for libraries like PyTorch, TensorFlow, or specific Python versions. |
| Experiment Setup | Yes | Unless otherwise specified, all methods are optimized using stochastic gradient descent with a fixed learning rate of 0.0001, weight decay of 10e-5, a view weighting factor α of 0.5, a batch size of 64. |