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Probabilistic Spatial Regression using a Deep Fully Convolutional Neural Network

Slabaugh, G. G., Knapp, K. & Al-Arif, S. M. (2017). Probabilistic Spatial Regression using a Deep Fully Convolutional Neural Network. In: Proceedings of the British Machine Vision Conference (BMVC). (154.1-154.12). BMVA Press.


Probabilistic predictions are often preferred in computer vision problems because they can provide a confidence of the predicted value. The recent dominant model for computer vision problems, the convolutional neural network, produces probabilistic output for classification and segmentation problems. But probabilistic regression using neural networks is not well defined. In this work, we present a novel fully convolutional neural network capable of producing a spatial probabilistic distribution for localizing image landmarks. We have introduced a new network layer and a novel loss function for the network to produce a two-dimensional probability map. The proposed network has been used in a novel framework to localize vertebral corners for lateral cervical Xray images. The framework has been evaluated on a dataset of 172 images consisting 797 vertebrae and 3,188 vertebral corners. The proposed framework has demonstrated promising performance in localizing vertebral corners, with a relative improvement of 38% over the previous state-of-the-art.

Publication Type: Book Section
Additional Information: Copyright the authors, 2017.
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments: School of Science & Technology > Computer Science
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