no code implementations • 30 Jun 2024 • Bas Peters, Eldad Haber, Keegan Lensink
Examples in hyperspectral land-use classification, airborne geophysical surveying, and seismic imaging illustrate that we can input large data volumes in one chunk and do not need to work on small patches, use dimensionality reduction, or employ methods that classify a patch to a single central pixel.
1 code implementation • 7 Jul 2020 • Keegan Lensink, Issam Laradji, Marco Law, Paolo Emilio Barbano, Savvas Nicolaou, William Parker, Eldad Haber
In this work we provide open source models for the segmentation of patterns of pulmonary opacification on chest Computed Tomography (CT) scans which have been correlated with various stages and severities of infection.
no code implementations • 7 Jul 2020 • Issam Laradji, Pau Rodriguez, Frederic Branchaud-Charron, Keegan Lensink, Parmida Atighehchian, William Parker, David Vazquez, Derek Nowrouzezahrai
We address this challenge introducing a scalable, fast, and accurate active learning system that accelerates the labeling of CT scan images.
3 code implementations • 4 Jul 2020 • Issam Laradji, Pau Rodriguez, Oscar Mañas, Keegan Lensink, Marco Law, Lironne Kurzman, William Parker, David Vazquez, Derek Nowrouzezahrai
Thus, we propose a consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images.
no code implementations • 16 Mar 2020 • Bas Peters, Eldad Haber, Keegan Lensink
The large spatial/frequency scale of hyperspectral and airborne magnetic and gravitational data causes memory issues when using convolutional neural networks for (sub-) surface characterization.
no code implementations • 14 Dec 2019 • Bas Peters, Eldad Haber, Keegan Lensink
Factors that limit the size of the input and output of a neural network include memory requirements for the network states/activations to compute gradients, as well as memory for the convolutional kernels or other weights.
no code implementations • 3 Oct 2019 • Jingrong Lin, Keegan Lensink, Eldad Haber
Generative Adversarial Networks have been shown to be powerful in generating content.
no code implementations • 24 May 2019 • Keegan Lensink, Bas Peters, Eldad Haber
However, their application to problems with high dimensional input and output, such as high-resolution image and video segmentation or 3D medical imaging, has been limited by various factors.
1 code implementation • 6 Mar 2019 • Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto
Deep convolutional neural networks have revolutionized many machine learning and computer vision tasks, however, some remaining key challenges limit their wider use.