Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |