Index

PowerPoint Presentation describer. Machine learning methods to automatically generate business captions from graphics

Emotional states are strong influential factors of humans’ choices, activities, and desires. They can be evaluated from face, self-observing reports and, what this thesis focuses on, speech. While there is some research done in speech emotion recognition it has less exploitation of deep learning approaches due to the field’s recentness and recent improvements in computational and optimizational approaches. In addition, the complicatedness of collecting improvised data, not from professional adult actors remains present in the state-of-the-art literature. Thus, the goal of this thesis is to explore the area of speech emotion recognition in children by testing the predominant approaches of neural networks with temporal prosody as well as abruptly expanding Transformers methods. We investigate the potential of transfer knowledge applied from adults’ to children’s data as the mechanism of dealing with lacking data. From the outcomes, we observe the improvement in the opportunities of transfer knowledge when gender and cultural aspects are included into the classification of emotions. Emotionally intelligent systems built based on the experiments described in the thesis can benefit the fields of remote monitoring or telemedicine for psychologists and pediatrists, teaching emotional intelligence for autistic children, and improving children’s health diagnostics and scanning procedures.

Classical Acoustic Markers for Depression in Parkinson’s Disease

Parkinson’s disease (PD) patients are commonly recognized for their tremors, although there is a wide range of different symptoms of PD. This is a progressive neurological condition, where patients do not have enough dopamine in the substancia nigra, which plays a role in motor control, mood, and cognitive functions. A really underestimated type of symptoms in PD is the mental and behavioral issues, which can manifest in depression, fatigue, or dementia. Clinical depression is a psychiatric mood disorder, caused by an individual’s difficulty in coping with stressful life events, and presents persistent feelings of sadness, negativity, and difficulty managing everyday responsibilities. This can be triggered by the lack of dopamine from PD, the upsetting and stressful situation of the Parkinson’s diagnosis as well as by the loneliness and isolation that can be caused by the Parkinson’s symptoms.
The goal of this work is to find the most suitable acoustic features that can discriminate against depression in Parkinson’s patients. Those features will be based on classical and interpretable acoustic descriptors.

Detection of Arterial Occlusion on MRI Angiography of the Lower Limbs using Deep Learning

Proposal Tri Nguyen

Automated detection and defect recognition of photovoltaic modules in photoluminescence videos

Automatic Detection of Microorganisms on Microscopic Images of Fluid Samples using Machine Learning

The objective of this thesis is to apply machine learning tools to rapidly analyze large datasets of microscopic images to identify and classify microbial infections.
Microorganisms can cause a wide range of diseases, e.g. tuberculosis, and left untreated, infections can quickly become fatal [1]. Fortunately, the discovery of penicillin and the subsequent invention of other antibiotics has significantly decreased the lethality of these diseases. This early success has led to an era of frequent use of antibiotics [2]. However, overprescription of these drugs has caused bacteria to develop mechanisms that confer resistance against particular drugs. This emergence of multi-drug resistance strains poses an extreme risk [2]. Therefore, these developments necessitate a more targeted application of antibiotics, which, however, requires the classification and characterization of bacteria found in patient samples. Existing methods for classifying microorganisms can be categorized into chemical, physical, molecular biological, and morphological methods [1]. While the latter is positioned to be the most direct and cost-effective method of the four, it requires a high amount of manual work and is thereby laborious and time-consuming [1].
The Weiss group develops and applies technologies for rapidly imaging and analyzing biological samples using high-resolution fluorescence microscopy, microfluidics. In collaboration with the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg, this toolkit is extended by computer vision components to analyze the image data.
Computer vision tools are well-suited for image classification problems and have been widely applied to microscopy. On relatively pure laboratory samples these tools can perform astonishingly well [1]. However, a single droplet of a fluid from a patient sample is significantly more challenging as it can contain a large array of different types of objects in very large quantities which complicates the detection and classification of single objects. Under the assumption that high quality and high-resolution microscopy images are provided, the key challenges are thus twofold: first to find the objects of interest and second to classify them.
In general, various approaches to solving this problem can be considered:
– Pursuing a supervised approach by establishing a large, bounding box or segment annotated database to train a deep neural net that can process unseen data and extract location and class of objects.
– Following semi-supervised techniques by only establishing a small, annotated database to train the classifier and using uniform or random segments of the input data.
– Processing uniform or random segments unsupervised by clustering them in multiple distinct classes.
In this thesis, we will investigate these strategies to address the problem of automatic object detection in microscopic images of fluid samples.
The thesis comprises the following tasks:
– literature review concerning sources and state-of-the-art approaches to construct classifiers with limited data available
– Implementation of one or more solutions to address the classification problem
– Evaluation of proposed method and emerging challenges
– Documentation and presentation of the findings, documentation of code
– Discussion of progress in weekly meetings with mentors Dr. Lucien Weiss and Frauke Wilm
[1] Zhang, Jinghua, Chen Li, and Marcin Grzegorzek. “Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer.” arXiv preprint arXiv:2108.00358 (2021).
[2] Casadevall, Arturo. “Crisis in infectious diseases: time for a new paradigm?.” Clinical infectious diseases 23.4 (1996): 790-794.