# End-to-end detection-segmentation network with ROI convolution

arXiv: https://arxiv.org/abs/1801.02722 · HTML: https://mlsanity.com/paper/1801.02722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2018.

## Built on by (most cited first, 1 of 1)

- [Joint Landmark and Structure Learning for Automatic Evaluation of Developmental Dysplasia of the Hip](https://mlsanity.com/paper/2106.05458/) (2021)

## Abstract (arXiv)

We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. We test the proposed method on a segmentation task of small objects on a clinical dataset of ultrasound images. We show that by jointly learning for detection and segmentation, the proposed network is able to improve the segmentation accuracy compared to only learning for segmentation. Code is publicly available at https://github.com/vincentzhang/roi-fcn.