3,564 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
SpotNet - Learned iterations for cell detection in image-based immunoassays
Accurate cell detection and counting in the image-based ELISpot and
FluoroSpot immunoassays is a challenging task. Recently proposed methodology
matches human accuracy by leveraging knowledge of the underlying physical
process of these assays and using proximal optimization methods to solve an
inverse problem. Nonetheless, thousands of computationally expensive iterations
are often needed to reach a near-optimal solution. In this paper, we exploit
the structure of the iterations to design a parameterized computation graph,
SpotNet, that learns the patterns embedded within several training images and
their respective cell information. Further, we compare SpotNet to a
convolutional neural network layout customized for cell detection. We show
empirical evidence that, while both designs obtain a detection performance on
synthetic data far beyond that of a human expert, SpotNet is easier to train
and obtains better estimates of particle secretion for each cell.Comment: 5 pages, 4 figures, 2019 IEEE 16th International Symposium on
Biomedical Imaging (ISBI 2019), Venice, Italy, April 8-11, 201
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