Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation
Authors: Wenfang Yao, Chen Liu, Kejing Yin, William Cheung, Jing Qin
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments using MIMIC datasets show that the proposed model could effectively address asynchronicity in multimodal fusion and consistently outperform existing methods. |
| Researcher Affiliation | Academia | 1School of Nursing, The Hong Kong Polytechnic University 2Department of Computer Science, Hong Kong Baptist University 3School of Software Engineering, South China University of Technology |
| Pseudocode | No | The paper describes methods in prose, but does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Chenliu-svg/DDL-CXR. |
| Open Datasets | Yes | We empirically evaluate the clinical predictive performance of DDL-CXR using MIMIC-IV [50] and MIMIC-CXR [18]4. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. |
| Dataset Splits | Yes | The dataset is randomly split by the patient identifier with a ratio of 24:4:7 for training, validation, and testing, which avoids patient overlapping between subsets. |
| Hardware Specification | Yes | The training and validation processes are executed on a server equipped with a RTX 4090-24GB GPU card and a 16 v CPU Intel Xeon Processor. |
| Software Dependencies | Yes | The method is implemented using Py Torch 1.9.1 and Py Torch-Lightning 1.4.2. |
| Experiment Setup | Yes | The Transformer f EHR cond is designed with one layer, a model dimension d set to 128, and a maximum EHR data length of 70. The UNet model ϵθ features an input channel of 8 and an output channel of 4. [...] The model is trained for 200 epochs with a batch size of 32, and the model with the smallest composite loss on the validation set is selected for subsequent latent Chest X-ray (CXR) generation. We set the hyperparameters α to 0.2, and β to 0.5, empirically. |