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 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |