Metric Learning on Healthcare Data with Incomplete Modalities
Authors: Qiuling Suo, Weida Zhong, Fenglong Ma, Ye Yuan, Jing Gao, Aidong Zhang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed framework learns more accurate distance metric on real-world healthcare datasets with incomplete modalities, comparing with the state-of-the-art approaches. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, SUNY at Buffalo, NY, USA 2College of Information and Communication Engineering, Beijing University of Technology, China 3Department of Computer Science, University of Virginia, VA, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The Alzheimer s Disease Neuroimaging Initiative (ADNI) 1 project aims to track the progression of Alzheimer s disease using biomarkers and clinical measures. There are 840 patients in three cohorts: 231 cognitively normal (CN), 410 mild cognitive impairment (MCI), and 199 Alzheimer s disease (AD) patients. In this work, we use the available modalities in the database: MRI images and PET images, and generate the missing images from each other. and 1https://adni.loni.usc.edu/ |
| Dataset Splits | Yes | We randomly divide the patient set into training, validation and testing sets in a 0.75:0.05:0.2 ratio. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions 'SPM2 and CAT12' softwares but does not provide specific version numbers for these or other key software dependencies used in their implementation. |
| Experiment Setup | No | The paper states "We set the learning rate and the network structures the same as [Cai et al., 2018] but in a 2D fashion," but it does not explicitly list these concrete hyperparameter values or network structure details within its own main text. |