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..
ElixirNet: Relation-Aware Network Architecture Adaptation for Medical Lesion Detection
Authors: Chenhan Jiang, Shaoju Wang, Xiaodan Liang, Hang Xu, Nong Xiao11093-11100
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Deep Lesion and Kits19 prove the effectiveness of Elixir Net, achieving improvement of both sensitivity and precision over FPN with fewer parameters. |
| Researcher Affiliation | Collaboration | Chenhan Jiang,2 Shaoju Wang,1 Hang Xu,2 Xiaodan Liang,1 Nong Xiao1 1Sun Yat-Sen University, 2Huawei Noah s Ark Lab |
| Pseudocode | No | The paper describes its methods verbally and with architectural diagrams but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is released or available in supplementary materials. |
| Open Datasets | Yes | We conduct experiments on the Deep Lesion (Yan et al. 2018a) and Kits19 (Heller et al. 2019) datasets. |
| Dataset Splits | Yes | We select 30% as validation (4889 lesions) and test (4927 lesions), while the rest is regarded as the training set (22919 images) following the official division (Yan, Bagheri, and Summers 2018; Yan et al. 2018a). |
| Hardware Specification | Yes | We conduct all experiments on a single server with 4 GTX1080 cards in Pytorch (Paszke et al. 2017). |
| Software Dependencies | No | The paper mentions 'Pytorch (Paszke et al. 2017)' but does not provide specific version numbers for Pytorch or other ancillary software components. |
| Experiment Setup | Yes | Network weights ωis updated after training 15 epochs with batch size =16 (same with validation sets). We choose momentum SGD with initial learning rate 0.02 (annealed down to zero following a cosine schedule), momentum 0.9, and weight decay 3 10 4 as an optimizer for weights ω, while architecture weights α are optimized by Adam with initial learning rate 3 10 4 , momentum 0.999 and weight decay 10 3. |