11 research outputs found
Towards autonomous diagnostic systems with medical imaging
Democratizing access to high quality healthcare has highlighted the need for autonomous diagnostic systems that a non-expert can use. Remote communities, first responders and even deep space explorers will come to rely on medical imaging systems that will provide them with Point of Care diagnostic capabilities.
This thesis introduces the building blocks that would enable the creation of such a system. Firstly, we present a case study in order to further motivate the need and requirements of autonomous diagnostic systems. This case study primarily concerns deep space exploration where astronauts cannot rely on communication with earth-bound doctors to help them through diagnosis, nor can they make the trip back to earth for treatment. Requirements and possible solutions about the major challenges faced with such an application are discussed.
Moreover, this work describes how a system can explore its perceived environment by developing a Multi Agent Reinforcement Learning method that allows for implicit communication between the agents. Under this regime agents can share the knowledge that benefits them all in achieving their individual tasks. Furthermore, we explore how systems can understand the 3D properties of 2D depicted objects in a probabilistic way.
In Part II, this work explores how to reason about the extracted information in a causally enabled manner. A critical view on the applications of causality in medical imaging, and its potential uses is provided. It is then narrowed down to estimating possible future outcomes and reasoning about counterfactual outcomes by embedding data on a pseudo-Riemannian manifold and constraining the latent space by using the relativistic concept of light cones.
By formalizing an approach to estimating counterfactuals, a computationally lighter alternative to the abduction-action-prediction paradigm is presented through the introduction of Deep Twin Networks. Appropriate partial identifiability constraints for categorical variables are derived and the method is applied in a series of medical tasks involving structured data, images and videos.
All methods are evaluated in a wide array of synthetic and real life tasks that showcase their abilities, often achieving state-of-the-art performance or matching the existing best performance while requiring a fraction of the computational cost.Open Acces
Utilizing Deep Reinforcement Learning to Effect Autonomous Orbit Transfers and Intercepts via Electromagnetic Propulsion
Problem: The growth in space-capable entities has caused a rapid rise in the number of derelict satellites and space debris in orbit around Earth, which pose a significant navigation hazard.
Objectives: Develop a system that is capable of autonomously neutralizing multiple pieces of space debris in various orbits
A Review of Causality for Learning Algorithms in Medical Image Analysis
Medical image analysis is a vibrant research area that offers doctors and
medical practitioners invaluable insight and the ability to accurately diagnose
and monitor disease. Machine learning provides an additional boost for this
area. However, machine learning for medical image analysis is particularly
vulnerable to natural biases like domain shifts that affect algorithmic
performance and robustness. In this paper we analyze machine learning for
medical image analysis within the framework of Technology Readiness Levels and
review how causal analysis methods can fill a gap when creating robust and
adaptable medical image analysis algorithms. We review methods using causality
in medical imaging AI/ML and find that causal analysis has the potential to
mitigate critical problems for clinical translation but that uptake and
clinical downstream research has been limited so far.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA)
https://www.melba-journal.org/papers/2022:028.html". ; Paper ID: 2022:02
Estimating Categorical Counterfactuals via Deep Twin Networks
Counterfactual inference is a powerful tool, capable of solving challenging
problems in high-profile sectors. To perform counterfactual inference, one
requires knowledge of the underlying causal mechanisms. However, causal
mechanisms cannot be uniquely determined from observations and interventions
alone. This raises the question of how to choose the causal mechanisms so that
resulting counterfactual inference is trustworthy in a given domain. This
question has been addressed in causal models with binary variables, but the
case of categorical variables remains unanswered. We address this challenge by
introducing for causal models with categorical variables the notion of
counterfactual ordering, a principle that posits desirable properties causal
mechanisms should posses, and prove that it is equivalent to specific
functional constraints on the causal mechanisms. To learn causal mechanisms
satisfying these constraints, and perform counterfactual inference with them,
we introduce deep twin networks. These are deep neural networks that, when
trained, are capable of twin network counterfactual inference -- an alternative
to the abduction, action, & prediction method. We empirically test our approach
on diverse real-world and semi-synthetic data from medicine, epidemiology, and
finance, reporting accurate estimation of counterfactual probabilities while
demonstrating the issues that arise with counterfactual reasoning when
counterfactual ordering is not enforced
Topological Data Analysis of Database Representations for Information Retrieval
Appropriately representing elements in a database so that queries may be
accurately matched is a central task in information retrieval. This recently
has been achieved by embedding the graphical structure of the database into a
manifold so that the hierarchy is preserved. Persistent homology provides a
rigorous characterization for the database topology in terms of both its
hierarchy and connectivity structure. We compute persistent homology on a
variety of datasets and show that some commonly used embeddings fail to
preserve the connectivity. Moreover, we show that embeddings which successfully
retain the database topology coincide in persistent homology. We introduce the
dilation-invariant bottleneck distance to capture this effect, which addresses
metric distortion on manifolds. We use it to show that distances between
topology-preserving embeddings of databases are small.Comment: 15 pages, 7 figure