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

Author
Zhang, Zichen
Tang, Min
Cobzas, Dana
Zonoobi, Dornoosh
Jagersand, Martin
Jaremko, Jacob L.
Faculty Advisor
Date
2018
Keywords
segmentation , detection , fully convolutional neural networks , ultrasound
Abstract (summary)
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.
Publication Information
Zichen Zhang, Min Tang, Dana Cobzas, Dornoosh Zonoobi, Martin Jagersand and Jacob Jaremko, End-To-End Detection-Segmentation Network with ROI Convolution, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
DOI
Notes
Item Type
Presentation
Language
English
Rights
All Rights Reserved