Multimodal Prototyping for cancer survival prediction
Authors: Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag Jayant Vaidya, Alexander Baras, Faisal Mahmood
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluation on six cancer types shows that our framework outperforms state-of-the-art methods with much less computation while unlocking new interpretability analyses. |
| Researcher Affiliation | Academia | 1Mass General Brigham, Boston, MA, USA. 2Harvard Medical School, Boston, MA, USA. 3Massachusetts Institute of Technology, Cambridge, MA, USA. 4Johns Hopkins University School of Medicine, Baltimore, MD, USA. |
| Pseudocode | No | The paper provides detailed explanations and mathematical derivations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https: //github.com/mahmoodlab/MMP. |
| Open Datasets | Yes | We use publicly available The Cancer Genome Atlas (TCGA) to evaluate MMP across six cancer types: Bladder urothelial carcinoma (BLCA) (n = 359), Breast invasive carcinoma (BRCA) (n = 868), Lung adenocarcinoma (LUAD) (n = 412), Stomach adenocarcinoma (STAD) (n = 318), Colon and Rectum adenocarcinoma (CRC) (n = 296), and Kidney renal clear cell carcinoma (KIRC) (n = 340). ... Log-2 transformed transcripts per million bulk RNA sequencing expression for all TCGA cohorts is accessed through UCSC Xena database (Goldman et al., 2020). |
| Dataset Splits | Yes | Following standard practice, we use 5-fold site-stratified cross-validation to mitigate batch effect (Howard et al., 2021). |
| Hardware Specification | No | The paper does not explicitly state the specific hardware (e.g., GPU models, CPU types, or memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions optimizers (Adam W) and libraries in citations (e.g., scikit-survival), but does not provide a reproducible description of ancillary software with specific version numbers for key components like programming languages or deep learning frameworks. |
| Experiment Setup | Yes | All models are trained with a 1 10 4 learning rate with cosine decay scheduler, Adam W optimizer, and 1 10 5 weight decay for 20 epochs. MMP uses the Cox loss with a batch size of 64. |