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..
Alternative Baselines for Low-Shot 3D Medical Image Segmentation—An Atlas Perspective
Authors: Shuxin Wang, Shilei Cao, Dong Wei, Cong Xie, Kai Ma, Liansheng Wang, Deyu Meng, Yefeng Zheng634-642
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments Since the tasks of segmenting brain anatomical structures and abdominal organs are noticeably different, we evaluate the Siamese-Baseline on brain anatomical structures (the CANDI Dataset (Kennedy et al. 2011)), and the IDA-Baseline on abdominal organs (the Multi-organ Dataset (Gibson et al. 2018; Roth et al. 2015; Clark et al. 2013; Xu et al. 2016)). For both datasets, we randomly select 20 volumes as test data, and use the others for training. The details of both datasets can be found in the supplementary material. |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science, Xiamen University, Xiamen, China 2 Tencent Jarvis Lab, Shenzhen, China 3 Department of Digestive Diseases, School of Medicine, Xiamen University, Xiamen, China 4 School of Mathematics and Statistics, Xi an Jiaotong University, Xi an, China |
| Pseudocode | No | The paper includes figures illustrating network architectures (e.g., Figure 1, Figure 2) and describes the methodology in text, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide an explicit statement about the availability of open-source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We evaluate the Siamese-Baseline on brain anatomical structures (the CANDI Dataset (Kennedy et al. 2011)), and the IDA-Baseline on abdominal organs (the Multi-organ Dataset (Gibson et al. 2018; Roth et al. 2015; Clark et al. 2013; Xu et al. 2016)). |
| Dataset Splits | No | The paper states, 'For both datasets, we randomly select 20 volumes as test data, and use the others for training.' This defines the train/test split, but no specific validation split percentages or sample counts are provided. |
| Hardware Specification | Yes | We train the Siamese-Baseline and IDABaseline on one NVIDIA Ge Force RTX 2080 Ti GPU with a single pair of volumes for each batch, on a workstation with Ubuntu 18.04.2 LTS and 251 GB memory. |
| Software Dependencies | Yes | All experiments are implemented with Keras 2.2.0 (Chollet et al. 2015) and Tensor Flow 1.10.0 (Abadi et al. 2016). |
| Experiment Setup | Yes | The network is trained with the Adam (Kingma and Ba 2014) optimizer with a learning rate of 0.0002 for the Siamese Baseline for 600 epochs and 0.0001 for the IDA-Baseline for 2,000 epochs. |