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Detection-aided liver lesion segmentation using deep learning

Author
Bellver, M.; Maninis, K.; Pont, J.; Giro, X.; Torres, J.; van Gool, L.
Type of activity
Presentation of work at congresses
Name of edition
Machine Learning for Health Workshop at NIPS 2017
Date of publication
2017
Presentation's date
2017-12-08
Book of congress proceedings
Advances in Neural Information Processing Systems 30 (NIPS 2017): NIPS Proceedingsß
First page
1
Last page
5
Project funding
Multimodal Signal Processing and Machine Learning on Graphs
Repository
http://hdl.handle.net/2117/120928 Open in new window
https://arxiv.org/abs/1711.11069 Open in new window
URL
https://ml4health.github.io/2017/pages/posters.html#session2 Open in new window
Abstract
A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the according treatments. In this work we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans using Convolutional Neural Networks (CNNs), that have proven good results in a variety of computer vision tasks, including medical imaging. The network that segments the lesions consists of a ...
Citation
Bellver, M., Maninis, K., Pont, J., Giro, X., Torres, J., Van Gool, L. Detection-aided liver lesion segmentation using deep learning. A: Machine Learning for Health Workshop at NIPS. "Advances in Neural Information Processing Systems 30 (NIPS 2017): NIPS Proceedingsß". 2017, p. 1-5.
Group of research
CAP - High Performace Computing Group
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants

  • Bellver, Míriam  (author and speaker )
  • Maninis, Kevis-Kokitsi  (author and speaker )
  • Pont Tuset, Jordi  (author and speaker )
  • Giro Nieto, Xavier  (author and speaker )
  • Torres Viñals, Jordi  (author and speaker )
  • van Gool, Luc  (author and speaker )

Attachments