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..
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 | 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. |