Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance
Authors: Mingcheng Qu, Guang Yang, Donglin Di, Tonghua Su, Yue Gao, Yang Song, Lei Fan
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative and qualitative experiments are conducted on five TCGA datasets, demonstrating that our model outperforms advanced methods by over 3.4% in C-Index performance. |
| Researcher Affiliation | Academia | 1Faculty of Computing, Harbin Institute of Technology 2School of Software, Tsinghua University 3School of Computer Science and Engineering, UNSW Sydney EMAIL |
| Pseudocode | No | The paper describes methods in text and equations but does not contain a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | Code: https: //github.com/MCPathology/MRe Path. |
| Open Datasets | Yes | We followed previous studies [Jaume et al., 2024; Zhang et al., 2024] and selected five datasets from The Cancer Genome Atlas (TCGA) to evaluate the performance of our model. The datasets include: Bladder Urothelial Carcinoma (BLCA) (n=384), Breast Invasive Carcinoma (BRCA) (n=968), Colon and Rectum Adenocarcinoma (COREAD) (n=298), Head and Neck Squamous Cell Carcinoma (HNSC) (n=392), and Stomach Adenocarcinoma (STAD) (n=317). |
| Dataset Splits | Yes | For each cancer type, we conducted 5-fold cross-validation, splitting the data into training and validation sets with a 4:1 ratio. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions a "pretrained encoder model (e.g., Res Net50)" and the "Adam optimizer" but does not specify software dependencies with version numbers (e.g., PyTorch 1.9, Python 3.8). |
| Experiment Setup | Yes | To ensure a fair comparison, we adopted similar settings as previous studies [Chen et al., 2021b; Jaume et al., 2024; Zhang et al., 2024], using identical dataset splits and employing the Adam optimizer with a learning rate of 1 × 10−4, a weight decay of 1 × 10−5, and 30 training epochs. |