| dc.description.abstract | Patients with schizophrenia often display impairments in the expression of emotion and speech
and those are observed in their facial behaviour. Such impairments present valuable information
for the psychiatrists, as they can be used for diagnosis. However, behaviour analysis
is subjective in clinical settings and time-consuming in research settings. In this thesis, our
aim is to develop fully-automatic methodologies for a) quantifying patient’s facial behaviour,
b) estimating symptom severity in schizophrenia, and c) determining whether the symptoms
have improved or not by a given treatment. In the analysis, videos of professional-patient interviews
of symptom assessment, that were recorded in realistic conditions, are used. This helps
in moving from controlled contexts used in the literature to similar-to-real clinical settings.
Firstly, an architecture is proposed for automatic facial expression analysis. The proposed architecture
address the data imbalance and threshold selection problems in multilabel classification,
and is trained using several datasets recorded in controlled environments. Then, the expression
analysis is moved from the controlled environments to the recent in-the-wild settings,
where VGG-16 networks are trained using 4 recent datasets captured in the wild. In-the-wild
analysis helps in analyzing more patients and leads to better results in symptom estimation.
Secondly, a deep learning approach is proposed for estimating expression-related symptoms
of schizophrenia in two different assessment interviews, namely PANSS and CAINS. The proposed
approach consists of Gaussian Mixture Model and Fisher Vector layers for extracting
compact statistical features over the whole video interview. Experiments show promising results
both on statistical analysis and symptom estimation. Finally, two methods are proposed
for addressing directly the problem of treatment outcome estimation in schizophrenia – more
specifically, are aimed at determining whether specific symptoms have improved or not by
analysing jointly two videos of the same patient, one before and one after the treatment. | en_US |
