Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Upping the Game: How 2D U-Net Skip Connections Flip 3D Segmentation
Authors: Xingru Huang, yihao guo, Jian Huang, Tianyun Zhang, HE HONG, Shaowei Jiang, Yaoqi Sun
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 52,338 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,810 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through rigorous experimental validation on five publicly accessible datasets FLARE2021, OIMHS, Fe TA2021, Abdomen CT-1K, and BTCV, the proposed method surpasses contemporary state-of-the-art models. |
| Researcher Affiliation | Academia | Xingru Huang1 , Yihao Guo1 , Jian Huang1 , Tianyun Zhang1, Hong He1 , Shaowei Jiang1 , Yaoqi Sun1 1Hangzhou Dianzi University |
| Pseudocode | No | The paper describes the proposed methods and modules textually and with diagrams but does not include explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is available at https://github.com/IMOP-lab/U-Shaped-Connection. |
| Open Datasets | Yes | Through rigorous experimental validation on five publicly accessible datasets FLARE2021, OIMHS, Fe TA2021, Abdomen CT-1K, and BTCV, the proposed method surpasses contemporary state-of-the-art models. |
| Dataset Splits | Yes | The datasets are randomly partitioned in an 8:1:1 ratio for training, validation, and testing. |
| Hardware Specification | Yes | The experiments are conducted on identical hardware and software environments, each workstation equipped with two NVIDIA Ge Force RTX 4090 GPUs and 128GB of memory. |
| Software Dependencies | Yes | The framework employs Python 3.9, Py Torch 2.0.0, and MONAI 0.9.0 within a Distributed Data-Parallel (DDP) training framework. |
| Experiment Setup | Yes | All training utilizes the LDice CE function with the Adam W [55] optimizer, a learning rate of 0.0001, 80,000 training iterations, and a batch size of 2. Data augmentation techniques, including random flip, random rotation, random scaling, and random 3D elastic transformation, are applied to enhance dataset diversity and model generalization. |