17 research outputs found
Microwave Imaging of Brain Stroke:Contributions to Modeling and Inverse Problem Resolution
Brain stroke is an age-related illness which has become a major issue in our ageing societies. Early diagnosis and treatment are of high importance for the full recovery of the patient, as reminded in Anglo-Saxon countries by the abbreviation FAST (Face, Arm, Speech, Time) referring to both the four major visible signs and the necessity to act fast. In this respect, Computed Tomography (CT) and Nuclear Magnetic Resonance (NMR) imaging are key diagnostic tools in clinical practice. Unfortunately, not only these modalities can neither be transported nor rapidly usable, which would allow early treatment (especially in rural environments), but also cannot be brought to the bedside of the patient to monitor the evolution of the disease.
Microwave Imaging (MWI) is a potential candidate to provide fast and accurate diagnostic insights for brain stroke pathological states. The head of the patient is illuminated with low-power microwave waveforms (non-ionizing radiations), whose backscattered signals are used to generate either images of its internal structures, distributions, patterns and shapes (qualitative imaging) or directly its physical parameters such as the dielectric contrast and the permittivity values (quantitative imaging). The technology relies on the high sensitivity of microwaves on the water content of tissues to allow for the discrimination between pathological and healthy regions.
This thesis focuses on both the forward modeling of the electromagnetic phenomena arising in biological tissues and the inverse scattering problem for imaging in the differential MWI (dMWI) scenario for brain stroke monitoring. It is intrinsically interdisciplinary as it requires knowledge in Biology, Medicine, Physics, Chemistry, and Engineering.
In order to investigate the challenges arising in brain MWI, it is crucial to have accurate and efficient solvers to model electromagnetic (EM) fields at UHF/SHF-bands. The head is a distributed, heterogeneous, and lossy scatterer for which existing solvers are known to struggle at higher frequencies. Volume Integral Equation (VIE) formulations and MultiGrid (MG) approaches are investigated to find the actual solution of the field distributions for large scale problems.
The EM modeling also permits to analyze the feasibility of brain MWI, which depends on the power transmission from the antennas towards the human brain. In order to estimate this transmission, simplified but still representative models, including intermediate layers -skin, fat, bone, and CerebroSpinal Fluid (CSF) - of the head, are proposed in the framework of simulations (analytical tools) and experimental validations (3D printed head phantom).
For the imaging task, the physics of the EM scattering, leads to complex non-linear inverse scattering problems (consisting in retrieving from a set of field measurements the physical parameters which produced them) for which reliable assumptions and approximations must be found. For brain MWI, estimating and quantifying the degree of non-linearity allows for determining the scope of application of existing algorithms, for which different regularizers
are applied.
Modeling and inverse problem resolution for brain MWI investigated in the present work are ultimately meant to contribute to the development of a technology dedicated to brain stroke detection, differentiation, and monitoring
FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection
Natural language processing (NLP) applications such as named entity
recognition (NER) for low-resource corpora do not benefit from recent advances
in the development of large language models (LLMs) where there is still a need
for larger annotated datasets. This research article introduces a methodology
for generating translated versions of annotated datasets through crosslingual
annotation projection. Leveraging a language agnostic BERT-based approach, it
is an efficient solution to increase low-resource corpora with few human
efforts and by only using already available open data resources. Quantitative
and qualitative evaluations are often lacking when it comes to evaluating the
quality and effectiveness of semi-automatic data generation strategies. The
evaluation of our crosslingual annotation projection approach showed both
effectiveness and high accuracy in the resulting dataset. As a practical
application of this methodology, we present the creation of French Annotated
Resource with Semantic Information for Medical Entities Detection (FRASIMED),
an annotated corpus comprising 2'051 synthetic clinical cases in French. The
corpus is now available for researchers and practitioners to develop and refine
French natural language processing (NLP) applications in the clinical field
(https://zenodo.org/record/8355629), making it the largest open annotated
corpus with linked medical concepts in French
Contributions to 3D differential microwave imaging
This paper presents new contributions to forward and inverse problems in 3D differential microwave imaging. This technique is well suited to monitor a pathological evolution, for instance the brain stroke of a patient. The forward problem is solved using a volume integral equation formulation, which enables to accurately compute the scattering by an heterogeneous medium such as the brain. In this contribution, several inversion regularization schemes are compared in order to retrieve the characteristics of the brain and more specifically to track the evolution of the pathology. These regularizations take advantage of the a priori sparsity knowledge inherent to the differential imaging scenario
InterpretTime: a new approach for the systematic evaluation of neural-network interpretability in time series classification
We present a novel approach to evaluate the performance of interpretability
methods for time series classification, and propose a new strategy to assess
the similarity between domain experts and machine data interpretation. The
novel approach leverages a new family of synthetic datasets and introduces new
interpretability evaluation metrics. The approach addresses several common
issues encountered in the literature, and clearly depicts how well an
interpretability method is capturing neural network's data usage, providing a
systematic interpretability evaluation framework. The new methodology
highlights the superiority of Shapley Value Sampling and Integrated Gradients
for interpretability in time-series classification tasks
Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review
Interoperability and secondary use of data is a challenge in health care. Specifically, the reuse of clinical free text remains an unresolved problem. The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) has become the universal language of health care and presents characteristics of a natural language. Its use to represent clinical free text could constitute a solution to improve interoperability
Numerical Assessment of Brain Stroke Follow-Up via Differential Microwave Imaging
This paper proposes a numerical assessment of brain stroke follow-up via differential Microwave Imaging. The aim is to follow the evolution of the patient’s stroke growth and location, in order to support decisions in the clinical treatment. We present a fully 3D algorithm for Differential Microwave Imaging with arbitrary illuminations for which the vector nature of the equations is conserved, thus not limited to the simple cases of TE or TM excitations. Thanks to the effectiveness and flexibility of the adopted forward modelling tool, the approach can handle complex scenarios and large grid
Volume Integral Equation Formulation for Medical Applications
In recent years, there is a considerable and growing interest in developing fast integral equation methods for solving Maxwell’s equations. Volume integral equations are a versatile technique to model inhomogeneous scattering objects. Numerical tests show excellent convergence properties of the technique for strongly inhomogeneous media with high dielectric contrast
Evaluation of Document Retrieval Systems on a Medical Corpus in French: Indexation vs. Feature Learning
This paper presents five document retrieval systems for a small (few thousands) and domain specific corpora (weekly peer-reviewed medical journals published in French) as well as an evaluation methodology to quantify the models performance. The proposed methodology does not rely on external annotations and therefore can be used as an ad hoc evaluation procedure for most document retrieval tasks. Statistical models and vector space models are empirically compared on a synthetic document retrieval task. For our dataset size and specificities the statistical approaches consistently performed better than its vector space counterparts
Ambulatory Peritoneal Dialysis Analysis Framework
This paper presents the design of an autonomous tracking device to enhance understanding of ambulatory peritoneal dialysis. The resulting tool aims to serve as a framework for research analysis and a decision support for treatment adjustments in peritoneal dialysis
Caregivers Interactions with Clinical Autocomplete Tool: A Retrospective Study
Hospital caregivers report patient data while being under constant pressure. These records include structured information, with some of them being derived from a restricted list of terms. Finding the right term from a large terminology can be time-consuming, harming the clinician's productivity. To deal with this hurdle, an autocomplete system is employed, providing the closest terms after a prefix is typed. While this software application clearly smoothens the term searching, this paper studies the influences of the tool on caregivers' reporting, inspecting the evolution of their typing conduct over time