53 research outputs found
Tensor Networks for Medical Image Classification
With the increasing adoption of machine learning tools like neural networks
across several domains, interesting connections and comparisons to concepts
from other domains are coming to light. In this work, we focus on the class of
Tensor Networks, which has been a work horse for physicists in the last two
decades to analyse quantum many-body systems. Building on the recent interest
in tensor networks for machine learning, we extend the Matrix Product State
tensor networks (which can be interpreted as linear classifiers operating in
exponentially high dimensional spaces) to be useful in medical image analysis
tasks. We focus on classification problems as a first step where we motivate
the use of tensor networks and propose adaptions for 2D images using classical
image domain concepts such as local orderlessness of images. With the proposed
locally orderless tensor network model (LoTeNet), we show that tensor networks
are capable of attaining performance that is comparable to state-of-the-art
deep learning methods. We evaluate the model on two publicly available medical
imaging datasets and show performance improvements with fewer model
hyperparameters and lesser computational resources compared to relevant
baseline methods.Comment: Accepted for publication at International Conference on Medical
Imaging with Deep Learning (MIDL), 2020. Reviews on Openreview here:
https://openreview.net/forum?id=jjk6bxk07
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
In this work, we adapt a method based on multiple hypothesis tracking (MHT)
that has been shown to give state-of-the-art vessel segmentation results in
interactive settings, for the purpose of extracting trees. Regularly spaced
tubular templates are fit to image data forming local hypotheses. These local
hypotheses are used to construct the MHT tree, which is then traversed to make
segmentation decisions. However, some critical parameters in this method are
scale-dependent and have an adverse effect when tracking structures of varying
dimensions. We propose to use statistical ranking of local hypotheses in
constructing the MHT tree, which yields a probabilistic interpretation of
scores across scales and helps alleviate the scale-dependence of MHT
parameters. This enables our method to track trees starting from a single seed
point. Our method is evaluated on chest CT data to extract airway trees and
coronary arteries. In both cases, we show that our method performs
significantly better than the original MHT method.Comment: Accepted for publication at the International Journal of Medical
Physics and Practic
A batch algorithm for estimating trajectories of point targets using expectation maximization
In this paper, we propose a strategy that is based on expectation maximization for tracking multiple point targets. The algorithm is similar to probabilistic multi-hypothesis tracking (PMHT) but does not relax the point target model assumptions. According to the point target models, a target can generate at most one measurement, and a measurement is generated by at most one target. With this model assumption, we show that the proposed algorithm can be implemented as iterations of Rauch-Tung-Striebel (RTS) smoothing for state estimation, and the loopy belief propagation method for marginal data association probabilities calculation. Using example illustrations with tracks, we compare the proposed algorithm with PMHT and joint probabilistic data association (JPDA) and show that PMHT and JPDA exhibit coalescence when there are closely moving targets whereas the proposed algorithm does not. Furthermore, extensive simulations c comparing the mean optimal subpattern assignment (MOSPA) performance of the algorithm for different scenarios averaged over several Monte Carlo iterations show that the proposed algorithm performs better than JPDA and PMHT. We also compare it to benchmarking algorithm: N-scan pruning based track-oriented multiple hypothesis tracking (TOMHT). The proposed algorithm shows a good tradeoff between computational complexity and the MOSPA performance
Locally orderless tensor networks for classifying two- and three-dimensional medical images
Tensor networks are factorisations of high rank tensors into networks of
lower rank tensors and have primarily been used to analyse quantum many-body
problems. Tensor networks have seen a recent surge of interest in relation to
supervised learning tasks with a focus on image classification. In this work,
we improve upon the matrix product state (MPS) tensor networks that can operate
on one-dimensional vectors to be useful for working with 2D and 3D medical
images. We treat small image regions as orderless, squeeze their spatial
information into feature dimensions and then perform MPS operations on these
locally orderless regions. These local representations are then aggregated in a
hierarchical manner to retain global structure. The proposed locally orderless
tensor network (LoTeNet) is compared with relevant methods on three datasets.
The architecture of LoTeNet is fixed in all experiments and we show it requires
lesser computational resources to attain performance on par or superior to the
compared methods.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) (see https://melba-journal.org). Source code at
https://github.com/raghavian/LoTeNet_pytorch
Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
Deep learning (DL) can achieve impressive results across a wide variety of
tasks, but this often comes at the cost of training models for extensive
periods on specialized hardware accelerators. This energy-intensive workload
has seen immense growth in recent years. Machine learning (ML) may become a
significant contributor to climate change if this exponential trend continues.
If practitioners are aware of their energy and carbon footprint, then they may
actively take steps to reduce it whenever possible. In this work, we present
Carbontracker, a tool for tracking and predicting the energy and carbon
footprint of training DL models. We propose that energy and carbon footprint of
model development and training is reported alongside performance metrics using
tools like Carbontracker. We hope this will promote responsible computing in ML
and encourage research into energy-efficient deep neural networks.Comment: Accepted to be presented at the ICML Workshop on "Challenges in
Deploying and monitoring Machine Learning Systems", 2020. Source code at this
link https://github.com/lfwa/carbontracker
Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI
Artificial Intelligence (AI) is currently spearheaded by machine learning
(ML) methods such as deep learning (DL) which have accelerated progress on many
tasks thought to be out of reach of AI. These ML methods can often be compute
hungry, energy intensive, and result in significant carbon emissions, a known
driver of anthropogenic climate change. Additionally, the platforms on which ML
systems run are associated with environmental impacts including and beyond
carbon emissions. The solution lionized by both industry and the ML community
to improve the environmental sustainability of ML is to increase the efficiency
with which ML systems operate in terms of both compute and energy consumption.
In this perspective, we argue that efficiency alone is not enough to make ML as
a technology environmentally sustainable. We do so by presenting three high
level discrepancies between the effect of efficiency on the environmental
sustainability of ML when considering the many variables which it interacts
with. In doing so, we comprehensively demonstrate, at multiple levels of
granularity both technical and non-technical reasons, why efficiency is not
enough to fully remedy the environmental impacts of ML. Based on this, we
present and argue for systems thinking as a viable path towards improving the
environmental sustainability of ML holistically.Comment: 24 pages; 6 figure
Lung Segmentation from Chest X-rays using Variational Data Imputation
Pulmonary opacification is the inflammation in the lungs caused by many
respiratory ailments, including the novel corona virus disease 2019 (COVID-19).
Chest X-rays (CXRs) with such opacifications render regions of lungs
imperceptible, making it difficult to perform automated image analysis on them.
In this work, we focus on segmenting lungs from such abnormal CXRs as part of a
pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the
high opacity regions as missing data and present a modified CNN-based image
segmentation network that utilizes a deep generative model for data imputation.
We train this model on normal CXRs with extensive data augmentation and
demonstrate the usefulness of this model to extend to cases with extreme
abnormalities.Comment: Accepted to be presented at the first Workshop on the Art of Learning
with Missing Values (Artemiss) hosted by the 37th International Conference on
Machine Learning (ICML). Source code, training data and the trained models
are available here: https://github.com/raghavian/lungVAE
Segmentation of roots in soil with U-Net
Demonstration of the feasibility of a U-Net based CNN system for segmenting images of roots in soil and for replacing the manual line-intersect method
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
Graph refinement, or the task of obtaining subgraphs of interest from
over-complete graphs, can have many varied applications. In this work, we
extract trees or collection of sub-trees from image data by, first deriving a
graph-based representation of the volumetric data and then, posing the tree
extraction as a graph refinement task. We present two methods to perform graph
refinement. First, we use mean-field approximation (MFA) to approximate the
posterior density over the subgraphs from which the optimal subgraph of
interest can be estimated. Mean field networks (MFNs) are used for inference
based on the interpretation that iterations of MFA can be seen as feed-forward
operations in a neural network. This allows us to learn the model parameters
using gradient descent. Second, we present a supervised learning approach using
graph neural networks (GNNs) which can be seen as generalisations of MFNs.
Subgraphs are obtained by training a GNN-based graph refinement model to
directly predict edge probabilities. We discuss connections between the two
classes of methods and compare them for the task of extracting airways from 3D,
low-dose, chest CT data. We show that both the MFN and GNN models show
significant improvement when compared to one baseline method, that is similar
to a top performing method in the EXACT'09 Challenge, and a 3D U-Net based
airway segmentation model, in detecting more branches with fewer false
positives.Comment: Accepted for publication at Medical Image Analysis. 14 page
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