59,591 research outputs found
Explainable Equivariant Neural Networks for Particle Physics: PELICAN
PELICAN is a novel permutation equivariant and Lorentz invariant or covariant
aggregator network designed to overcome common limitations found in
architectures applied to particle physics problems. Compared to many approaches
that use non-specialized architectures that neglect underlying physics
principles and require very large numbers of parameters, PELICAN employs a
fundamentally symmetry group-based architecture that demonstrates benefits in
terms of reduced complexity, increased interpretability, and raw performance.
We present a comprehensive study of the PELICAN algorithm architecture in the
context of both tagging (classification) and reconstructing (regression)
Lorentz-boosted top quarks, including the difficult task of specifically
identifying and measuring the -boson inside the dense environment of the
Lorentz-boosted top-quark hadronic final state. We also extend the application
of PELICAN to the tasks of identifying quark-initiated vs.~gluon-initiated
jets, and a multi-class identification across five separate target categories
of jets. When tested on the standard task of Lorentz-boosted top-quark tagging,
PELICAN outperforms existing competitors with much lower model complexity and
high sample efficiency. On the less common and more complex task of 4-momentum
regression, PELICAN also outperforms hand-crafted, non-machine learning
algorithms. We discuss the implications of symmetry-restricted architectures
for the wider field of machine learning for physics.Comment: 52 pages, 34 figures, 12 table
Deep Boosted Regression for MR to CT Synthesis
Attenuation correction is an essential requirement of positron emission
tomography (PET) image reconstruction to allow for accurate quantification.
However, attenuation correction is particularly challenging for PET-MRI as
neither PET nor magnetic resonance imaging (MRI) can directly image tissue
attenuation properties. MRI-based computed tomography (CT) synthesis has been
proposed as an alternative to physics based and segmentation-based approaches
that assign a population-based tissue density value in order to generate an
attenuation map. We propose a novel deep fully convolutional neural network
that generates synthetic CTs in a recursive manner by gradually reducing the
residuals of the previous network, increasing the overall accuracy and
generalisability, while keeping the number of trainable parameters within
reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT
pairs and a four-fold random bootstrapped validation with a 80:20 split is
performed. Quantitative results show that the proposed framework outperforms a
state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE)
from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction
error from 14.3% to 7.2%.Comment: Accepted at SASHIMI201
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Chest X-ray is one of the most accessible medical imaging technique for
diagnosis of multiple diseases. With the availability of ChestX-ray14, which is
a massive dataset of chest X-ray images and provides annotations for 14
thoracic diseases; it is possible to train Deep Convolutional Neural Networks
(DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we
experiment a set of deep learning models and present a cascaded deep neural
network that can diagnose all 14 pathologies better than the baseline and is
competitive with other published methods. Our work provides the quantitative
results to answer following research questions for the dataset: 1) What loss
functions to use for training DCNN from scratch on ChestX-ray14 dataset that
demonstrates high class imbalance and label co occurrence? 2) How to use
cascading to model label dependency and to improve accuracy of the deep
learning model?Comment: Submitted to CVPR 201
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