286 research outputs found
Label-driven weakly-supervised learning for multimodal deformable image registration
Spatially aligning medical images from different modalities remains a
challenging task, especially for intraoperative applications that require fast
and robust algorithms. We propose a weakly-supervised, label-driven formulation
for learning 3D voxel correspondence from higher-level label correspondence,
thereby bypassing classical intensity-based image similarity measures. During
training, a convolutional neural network is optimised by outputting a dense
displacement field (DDF) that warps a set of available anatomical labels from
the moving image to match their corresponding counterparts in the fixed image.
These label pairs, including solid organs, ducts, vessels, point landmarks and
other ad hoc structures, are only required at training time and can be
spatially aligned by minimising a cross-entropy function of the warped moving
label and the fixed label. During inference, the trained network takes a new
image pair to predict an optimal DDF, resulting in a fully-automatic,
label-free, real-time and deformable registration. For interventional
applications where large global transformation prevails, we also propose a
neural network architecture to jointly optimise the global- and local
displacements. Experiment results are presented based on cross-validating
registrations of 111 pairs of T2-weighted magnetic resonance images and 3D
transrectal ultrasound images from prostate cancer patients with a total of
over 4000 anatomical labels, yielding a median target registration error of 4.2
mm on landmark centroids and a median Dice of 0.88 on prostate glands.Comment: Accepted to ISBI 201
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving
In Autonomous Vehicles (AVs), one fundamental pillar is perception, which
leverages sensors like cameras and LiDARs (Light Detection and Ranging) to
understand the driving environment. Due to its direct impact on road safety,
multiple prior efforts have been made to study its the security of perception
systems. In contrast to prior work that concentrates on camera-based
perception, in this work we perform the first security study of LiDAR-based
perception in AV settings, which is highly important but unexplored. We
consider LiDAR spoofing attacks as the threat model and set the attack goal as
spoofing obstacles close to the front of a victim AV. We find that blindly
applying LiDAR spoofing is insufficient to achieve this goal due to the machine
learning-based object detection process. Thus, we then explore the possibility
of strategically controlling the spoofed attack to fool the machine learning
model. We formulate this task as an optimization problem and design modeling
methods for the input perturbation function and the objective function. We also
identify the inherent limitations of directly solving the problem using
optimization and design an algorithm that combines optimization and global
sampling, which improves the attack success rates to around 75%. As a case
study to understand the attack impact at the AV driving decision level, we
construct and evaluate two attack scenarios that may damage road safety and
mobility. We also discuss defense directions at the AV system, sensor, and
machine learning model levels.Comment: Accepted at the ACM Conference on Computer and Communications
Security (CCS), 201
An unsupervised approach to Geographical Knowledge Discovery using street level and street network images
Recent researches have shown the increasing use of machine learn-ing methods
in geography and urban analytics, primarily to extract features and patterns
from spatial and temporal data using a supervised approach. Researches
integrating geographical processes in machine learning models and the use of
unsupervised approacheson geographical data for knowledge discovery had been
sparse. This research contributes to the ladder, where we show how latent
variables learned from unsupervised learning methods on urbanimages can be used
for geographic knowledge discovery. In particular, we propose a simple approach
called Convolutional-PCA(ConvPCA) which are applied on both street level and
street network images to find a set of uncorrelated and ordered visual
latentcomponents. The approach allows for meaningful explanations using a
combination of geographical and generative visualisations to explore the latent
space, and to show how the learned representation can be used to predict urban
characteristics such as streetquality and street network attributes. The
research also finds that the visual components from the ConvPCA model achieves
similaraccuracy when compared to less interpretable dimension reduction
techniques.Comment: SigSpatial 2019 GeoA
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