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].
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
Authors: Vo Nguyen Le Duy, Shogo Iwazaki, Ichiro Takeuchi
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct several experiments to demonstrate the performance of the proposed method. |
| Researcher Affiliation | Academia | Vo Nguyen Le Duy Nagoya Institute of Technology and RIKEN EMAIL Shogo Iwazaki Nagoya Institute of Technology EMAIL Ichiro Takeuchi Nagoya University and RIKEN EMAIL |
| Pseudocode | Yes | Algorithm 1 parametric_SI_DNN and Algorithm 2 compute_solution_path |
| Open Source Code | Yes | Our implementation is available at https://github.com/vonguyenleduy/dnn_segmentation_selective_inference |
| Open Datasets | Yes | We examine the brain image dataset extracted from the dataset used in [4], which includes 939 images with tumors and 941 images without tumors. |
| Dataset Splits | No | The paper discusses testing with generated null images and real-world brain images, but it does not specify explicit training/validation/test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency versions (e.g., programming language versions, library versions, or specific solver versions) used for the experiments. |
| Experiment Setup | No | The paper specifies the significance level (0.05) and the basic CNN structure used, but it does not provide explicit hyperparameters (e.g., learning rate, batch size, optimizer settings, epochs) for training the DNN or details on model initialization. |