# Large Batch and Patch Size Training for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2210.13364 · HTML: https://mlsanity.com/paper/2210.13364/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [Don't Decay the Learning Rate, Increase the Batch Size](https://mlsanity.com/paper/1711.00489/) (2017)
- [Loss Functions for Top-k Error: Analysis and Insights](https://mlsanity.com/paper/1512.00486/) (2015)
- [MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning](https://mlsanity.com/paper/1910.09308/) (2019)
- [Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation](https://mlsanity.com/paper/2012.09279/) (2020)
- [MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets](https://mlsanity.com/paper/2108.02559/) (2021)
- [Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirements](https://mlsanity.com/paper/2111.13630/) (2021)
- [Fast and Accurate 3D Medical Image Segmentation with Data-swapping Method](https://mlsanity.com/paper/1812.07816/) (2018)

## Abstract (arXiv)

Multi-organ segmentation enables organ evaluation, accounts the relationship between multiple organs, and facilitates accurate diagnosis and treatment decisions. However, only few models can perform segmentation accurately because of the lack of datasets and computational resources. On AMOS2022 challenge, which is a large-scale, clinical, and diverse abdominal multiorgan segmentation benchmark, we trained a 3D-UNet model with large batch and patch sizes using multi-GPU distributed training. Segmentation performance tended to increase for models with large batch and patch sizes compared with the baseline settings. The accuracy was further improved by using ensemble models that were trained with different settings. These results provide a reference for parameter selection in organ segmentation.