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 [1].
Concept-Level Causal Explanation Method for Brain Function Network Classification
Authors: Jinduo Liu, Feipeng Wang, Junzhong Ji
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that our CLCEM can not only accurately identify brain regions related to specific brain diseases but also make decisions based on the concepts of these brain regions, which enables humans to understand the decision-making process without performance degradation. |
| Researcher Affiliation | Academia | Jinduo Liu1 , Feipeng Wang1 and Junzhong Ji1 1 Beijing University of Technology EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Model Training of CLCEM |
| Open Source Code | Yes | The code is available at https://github.com/bjutAILab/CLCEM. |
| Open Datasets | Yes | The dataset used in this paper is obtained from the ABIDE I database. It has both functional and structural brain imaging data of 1112 individuals including 539 autism spectrum disorder (ASD) patients and 573 typically developing (TD) controls, which were collected from 16 different sites around the world. |
| Dataset Splits | Yes | The dataset was randomly divided into 10 equal parts, and we conducted a 10-fold cross validation. |
| Hardware Specification | Yes | The hardware setup features a high-performance NVIDIA Ge Force RTX 3090 and RTX 3080 Ti GPUs, paired with an AMD Ryzen 9 5950X 16-Core Processor. |
| Software Dependencies | Yes | The system operates on Ubuntu 20.04 LTS, providing a stable foundation for our projects. It is equipped with 64GB of high-speed DDR4 RAM, ensuring exceptional responsiveness and efficiency. For deep learning operations, we use the Py Torch framework, version 1.12.1, which includes GPU support via CUDA version 11.4. |
| Experiment Setup | Yes | Algorithm 1 Model Training of CLCEM Input: Xt, Yt, Xv, Yv Parameter: nbepochs, lr, wd, λ Output: Θ |