# Deep Neural Patchworks: Coping with Large Segmentation Tasks

arXiv: https://arxiv.org/abs/2206.03210 · HTML: https://mlsanity.com/paper/2206.03210/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks](https://mlsanity.com/paper/1910.05338/) (2019)

## Built on by (most cited first, 2 of 2)

- [Scaling In-Context Segmentation with Hierarchical Supervision](https://mlsanity.com/paper/2604.12752/) (2026)
- [PatchMorph: A Stochastic Deep Learning Approach for Unsupervised 3D Brain Image Registration with Small Patches](https://mlsanity.com/paper/2312.06958/) (2023)

## Abstract (arXiv)

Convolutional neural networks are the way to solve arbitrary image segmentation tasks. However, when images are large, memory demands often exceed the available resources, in particular on a common GPU. Especially in biomedical imaging, where 3D images are common, the problems are apparent. A typical approach to solve this limitation is to break the task into smaller subtasks by dividing images into smaller image patches. Another approach, if applicable, is to look at the 2D image sections separately, and to solve the problem in 2D. Often, the loss of global context makes such approaches less effective; important global information might not be present in the current image patch, or the selected 2D image section. Here, we propose Deep Neural Patchworks (DNP), a segmentation framework that is based on hierarchical and nested stacking of patch-based networks that solves the dilemma between global context and memory limitations.