37,690 research outputs found
Impact of adversarial examples on deep learning models for biomedical image segmentation
Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples. Adversarial examples are carefully crafted samples that force machine learning models to make mistakes during testing time. These malicious samples have been shown to be highly effective in misguiding classification tasks. However, research on the influence of adversarial examples on segmentation is significantly lacking. Given that a large portion of medical imaging problems are effectively segmentation problems, we analyze the impact of adversarial examples on deep learning-based image segmentation models. Specifically, we expose the vulnerability of these models to adversarial examples by proposing the Adaptive Segmentation Mask Attack (ASMA). This novel algorithm makes it possible to craft targeted adversarial examples that come with (1) high intersection-over-union rates between the target adversarial mask and the prediction and (2) with perturbation that is, for the most part, invisible to the bare eye. We lay out experimental and visual evidence by showing results obtained for the ISIC skin lesion segmentation challenge and the problem of glaucoma optic disc segmentation. An implementation of this algorithm and additional examples can be found at https://github.com/utkuozbulak/adaptive-segmentation-mask-attack
First results from the LUCID-Timepix spacecraft payload onboard the TechDemoSat-1 satellite in Low Earth Orbit
The Langton Ultimate Cosmic ray Intensity Detector (LUCID) is a payload
onboard the satellite TechDemoSat-1, used to study the radiation environment in
Low Earth Orbit (635km). LUCID operated from 2014 to 2017, collecting
over 2.1 million frames of radiation data from its five Timepix detectors on
board. LUCID is one of the first uses of the Timepix detector technology in
open space, with the data providing useful insight into the performance of this
technology in new environments. It provides high-sensitivity imaging
measurements of the mixed radiation field, with a wide dynamic range in terms
of spectral response, particle type and direction. The data has been analysed
using computing resources provided by GridPP, with a new machine learning
algorithm that uses the Tensorflow framework. This algorithm provides a new
approach to processing Medipix data, using a training set of human labelled
tracks, providing greater particle classification accuracy than other
algorithms. For managing the LUCID data, we have developed an online platform
called Timepix Analysis Platform at School (TAPAS). This provides a swift and
simple way for users to analyse data that they collect using Timepix detectors
from both LUCID and other experiments. We also present some possible future
uses of the LUCID data and Medipix detectors in space.Comment: Accepted for publication in Advances in Space Researc
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