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 Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine
Authors: Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate MM-Lego (Lego Merge and Lego Fuse) and its components (Lego Block) on seven multimodal medical datasets from three separate studies: The Cancer Genome Atlas (TCGA) (Institute, 2006), Medical Information Mart for Intensive Care (MIMIC) (Johnson et al., 2016) and the International Skin Imaging Collaboration (ISIC)) (Collaboration, 2020). |
| Researcher Affiliation | Academia | Konstantin Hemker1, Nikola Simidjievski2,1 & Mateja Jamnik1 1Department of Computer Science & Technology 2PBCI, Department of Oncology University of Cambridge Cambridge, UK EMAIL |
| Pseudocode | No | The paper describes methods with equations and figures but does not contain a distinct pseudocode or algorithm block. |
| Open Source Code | Yes | The code implementation for MM-Lego is available at https://github.com/konst-int-i/ mm-lego. |
| Open Datasets | Yes | We evaluate MM-Lego (Lego Merge and Lego Fuse) and its components (Lego Block) on seven multimodal medical datasets from three separate studies: The Cancer Genome Atlas (TCGA) (Institute, 2006), Medical Information Mart for Intensive Care (MIMIC) (Johnson et al., 2016) and the International Skin Imaging Collaboration (ISIC)) (Collaboration, 2020). |
| Dataset Splits | Yes | For each experiment and dataset, we perform a 5-fold repeated random sub-sampling with a 70-15-15 train-test-validation split. |
| Hardware Specification | Yes | The experiments were run on a single Nvidia A100 80GB GPU on a Ubuntu 22.04 virtual machine. |
| Software Dependencies | No | The experiments were run on a single Nvidia A100 80GB GPU on a Ubuntu 22.04 virtual machine. This mentions the operating system but does not specify software libraries with version numbers. |
| Experiment Setup | Yes | Scope Parameter Value Learning Rate 0.003 Epochs 40 Early Stopping Patience 7 L1 Regularization 0.0002 Batch size 512 Optimizer Adam LR Scheduler Reduce LROn Plateau |