3 research outputs found
Exploring variability in medical imaging
Although recent successes of deep learning and novel machine learning techniques improved the perfor-
mance of classification and (anomaly) detection in computer vision problems, the application of these
methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this
is the amount of variability that is encountered and encapsulated in human anatomy and subsequently
reflected in medical images. This fundamental factor impacts most stages in modern medical imaging
processing pipelines.
Variability of human anatomy makes it virtually impossible to build large datasets for each disease
with labels and annotation for fully supervised machine learning. An efficient way to cope with this is
to try and learn only from normal samples. Such data is much easier to collect. A case study of such
an automatic anomaly detection system based on normative learning is presented in this work. We
present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative
models, which are trained only utilising normal/healthy subjects.
However, despite the significant improvement in automatic abnormality detection systems, clinical
routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis
and localise abnormalities. Integrating human expert knowledge into the medical imaging processing
pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per-
spective of building an automated medical imaging system, it is still an open issue, to what extent
this kind of variability and the resulting uncertainty are introduced during the training of a model
and how it affects the final performance of the task. Consequently, it is very important to explore the
effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as
on the model’s performance in a specific machine learning task. A thorough investigation of this issue
is presented in this work by leveraging automated estimates for machine learning model uncertainty,
inter-observer variability and segmentation task performance in lung CT scan images.
Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging
was attempted. This state-of-the-art survey includes both conventional pattern recognition methods
and deep learning based methods. It is one of the first literature surveys attempted in the specific
research area.Open Acces
Learning for text mining : tackling the cost of feature and knowledge engineering.
Over the last decade, the state-of-the-art in text mining has moved
towards the adoption of machine learning as the main paradigm at the
heart of approaches. Despite significant advances, machine learning based
text mining solutions remain costly to design, develop and maintain
for real world problems. An important component of such cost
(feature engineering) concerns the effort required to understand which
features or characteristics of the data can be successfully exploited in
inducing a predictive model of the data. Another important component
of the cost (knowledge engineering) has to do with the effort in creating
labelled data, and in eliciting knowledge about the mining systems and
the data itself.
I present a series of approaches, methods and findings aimed at reducing
the cost of creating and maintaining document classification and
information extraction systems. They address the following questions:
Which classes of features lead to an improved classification accuracy in
the document classification and entity extraction tasks? How to reduce
the amount of labelled examples needed to train machine learning based
document classification and information extraction systems, so
as to relieve domain experts from this costly task? How to effectively
represent knowledge about these systems and the data that they manipulate,
in order to make systems interoperable and results replicable?
I provide the reader with the background information necessary to
understand the above questions and the contributions to the state-of the-
art contained herein. The contributions include: the identification
of novel classes of features for the document classification task which
exploit the multimedia nature of documents and lead to improved
classification accuracy; a novel approach to domain adaptation for
text categorization which outperforms standard supervised and semi-supervised
methods while requiring considerably less supervision;
and a well-founded formalism for declaratively specifying text and
multimedia mining systems
Fuelling the zero-emissions road freight of the future: routing of mobile fuellers
The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios