Towards Unsupervised Domain Adaptation for Diabetic Retinopathy Detection in the Tromsø Eye Study

Diabetic retinopathy (DR) is an eye disease which affects a third of the diabetic population. It is a preventable disease, but requires early detection for efficient treatment. While there has been increasing interest in applying deep learning techniques for DR detection in order to aid practitioners make more accurate diagnosis, these efforts are mainly focused on datasets that have been collected or created with ML in mind. In this thesis, however, we take a look at two particular datasets that have been collected at the University Hospital of North-Norway - UNN. These datasets have inherent problems that motivate the methodological choices in this work such as a variable number of input images and domain shift. We therefore contribute a multi-stream model for DR classification. The multi-stream model can model dependency across different images, can take in a variable of input of any size, is general in its detection such that the image processing is equal no matter which stream the image enters, and is compatible with the domain adaptation method ADDA, but we argue the model is compatible with many other methods. As a remedy for these problems, we propose a multi-stream deep learning architecture that is uniquely tailored to these datasets and illustrate how domain adaptation might be utilized within the framework to learn efficiently in the presence of domain shift. Our experiments demonstrates the models properties empirically, and shows it can deal with each of the presented problems. The model this paper contributes is a first step towards DR detection from these local datasets and, in the bigger picture, similar datasets worldwide

Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning

We propose a novel neural pipeline, MSGazeNet, that learns gaze representations by taking advantage of the eye anatomy information through a multistream framework. Our proposed solution comprises two components, first a network for isolating anatomical eye regions, and a second network for multistream gaze estimation. The eye region isolation is performed with a U-Net style network which we train using a synthetic dataset that contains eye region masks for the visible eyeball and the iris region. The synthetic dataset used in this stage is procured using the UnityEyes simulator, and consists of 80,000 eye images. Successive to training, the eye region isolation network is then transferred to the real domain for generating masks for the real-world eye images. In order to successfully make the transfer, we exploit domain randomization in the training process, which allows for the synthetic images to benefit from a larger variance with the help of augmentations that resemble artifacts. The generated eye region masks along with the raw eye images are then used together as a multistream input to our gaze estimation network, which consists of wide residual blocks. The output embeddings from these encoders are fused in the channel dimension before feeding into the gaze regression layers. We evaluate our framework on three gaze estimation datasets and achieve strong performances. Our method surpasses the state-of-the-art by 7.57% and 1.85% on two datasets, and obtains competitive results on the other. We also study the robustness of our method with respect to the noise in the data and demonstrate that our model is less sensitive to noisy data. Lastly, we perform a variety of experiments including ablation studies to evaluate the contribution of different components and design choices in our solution.Comment: 15 pages, 7 figures, 14 tables. This work has been accepted to the IEEE Transactions on Artificial Intelligence $\copyright$ 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other use

An Information Theoretic Approach to Speaker Diarization of Meeting Recordings

In this thesis we investigate a non parametric approach to speaker diarization for meeting recordings based on an information theoretic framework. The problem is formulated using the Information Bottleneck (IB) principle. Unlike other approaches where the distance between speaker segments is arbitrarily introduced, the IB method seeks the partition that maximizes the mutual information between observations and variables relevant for the problem while minimizing the distortion between observations. The distance between speech segments is selected as the Jensen-Shannon divergence as it arises from the IB objective function optimization. In the first part of the thesis, we explore IB based diarization with Mel frequency cepstral coefficients (MFCC) as input features. We study issues related to IB based speaker diarization such as optimizing the IB objective function, criteria for inferring the number of speakers. Furthermore, we benchmark the proposed system against a state-of-the-art systemon the NIST RT06 (Rich Transcription) meeting data for speaker diarization. The IB based system achieves similar speaker error rates (16.8%) as compared to a baseline HMM/GMM system (17.0%). This approach being non parametric clustering, perform diarization six times faster than realtime while the baseline is slower than realtime. The second part of thesis proposes a novel feature combination system in the context of IB diarization. Both speaker clustering and speaker realignment steps are discussed. In contrary to current systems, the proposed method avoids the feature combination by averaging log-likelihood scores. Two different sets of features were considered – (a) combination of MFCC features with time delay of arrival features (b) a four feature stream combination that combines MFCC, TDOA, modulation spectrum and frequency domain linear prediction. Experiments show that the proposed system achieve 5% absolute improvement over the baseline in case of two feature combination, and 7% in case of four feature combination. The increase in algorithm complexity of the IB system is minimal with more features. The system with four feature input performs in real time that is ten times faster than the GMM based system

Current trends in multilingual speech processing

In this paper, we describe recent work at Idiap Research Institute in the domain of multilingual speech processing and provide some insights into emerging challenges for the research community. Multilingual speech processing has been a topic of ongoing interest to the research community for many years and the field is now receiving renewed interest owing to two strong driving forces. Firstly, technical advances in speech recognition and synthesis are posing new challenges and opportunities to researchers. For example, discriminative features are seeing wide application by the speech recognition community, but additional issues arise when using such features in a multilingual setting. Another example is the apparent convergence of speech recognition and speech synthesis technologies in the form of statistical parametric methodologies. This convergence enables the investigation of new approaches to unified modelling for automatic speech recognition and text-to-speech synthesis (TTS) as well as cross-lingual speaker adaptation for TTS. The second driving force is the impetus being provided by both government and industry for technologies to help break down domestic and international language barriers, these also being barriers to the expansion of policy and commerce. Speech-to-speech and speech-to-text translation are thus emerging as key technologies at the heart of which lies multilingual speech processin

Audio-Visual Automatic Speech Recognition Using PZM, MFCC and Statistical Analysis

Audio-Visual Automatic Speech Recognition (AV-ASR) has become the most promising research area when the audio signal gets corrupted by noise. The main objective of this paper is to select the important and discriminative audio and visual speech features to recognize audio-visual speech. This paper proposes Pseudo Zernike Moment (PZM) and feature selection method for audio-visual speech recognition. Visual information is captured from the lip contour and computes the moments for lip reading. We have extracted 19th order of Mel Frequency Cepstral Coefficients (MFCC) as speech features from audio. Since all the 19 speech features are not equally important, therefore, feature selection algorithms are used to select the most efficient features. The various statistical algorithm such as Analysis of Variance (ANOVA), Kruskal-wallis, and Friedman test are employed to analyze the significance of features along with Incremental Feature Selection (IFS) technique. Statistical analysis is used to analyze the statistical significance of the speech features and after that IFS is used to select the speech feature subset. Furthermore, multiclass Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naive Bayes (NB) machine learning techniques are used to recognize the speech for both the audio and visual modalities. Based on the recognition rate combined decision is taken from the two individual recognition systems. This paper compares the result achieved by the proposed model and the existing model for both audio and visual speech recognition. Zernike Moment (ZM) is compared with PZM and shows that our proposed model using PZM extracts better discriminative features for visual speech recognition. This study also proves that audio feature selection using statistical analysis outperforms methods without any feature selection technique

Automatic recognition of multiparty human interactions using dynamic Bayesian networks

Relating statistical machine learning approaches to the automatic analysis of multiparty communicative events, such as meetings, is an ambitious research area. We have investigated automatic meeting segmentation both in terms of “Meeting Actions” and “Dialogue Acts”. Dialogue acts model the discourse structure at a fine grained level highlighting individual speaker intentions. Group meeting actions describe the same process at a coarse level, highlighting interactions between different meeting participants and showing overall group intentions. A framework based on probabilistic graphical models such as dynamic Bayesian networks (DBNs) has been investigated for both tasks. Our first set of experiments is concerned with the segmentation and structuring of meetings (recorded using multiple cameras and microphones) into sequences of group meeting actions such as monologue, discussion and presentation. We outline four families of multimodal features based on speaker turns, lexical transcription, prosody, and visual motion that are extracted from the raw audio and video recordings. We relate these lowlevel multimodal features to complex group behaviours proposing a multistreammodelling framework based on dynamic Bayesian networks. Later experiments are concerned with the automatic recognition of Dialogue Acts (DAs) in multiparty conversational speech. We present a joint generative approach based on a switching DBN for DA recognition in which segmentation and classification of DAs are carried out in parallel. This approach models a set of features, related to lexical content and prosody, and incorporates a weighted interpolated factored language model. In conjunction with this joint generative model, we have also investigated the use of a discriminative approach, based on conditional random fields, to perform a reclassification of the segmented DAs. The DBN based approach yielded significant improvements when applied both to the meeting action and the dialogue act recognition task. On both tasks, the DBN framework provided an effective factorisation of the state-space and a flexible infrastructure able to integrate a heterogeneous set of resources such as continuous and discrete multimodal features, and statistical language models. Although our experiments have been principally targeted on multiparty meetings; features, models, and methodologies developed in this thesis can be employed for a wide range of applications. Moreover both group meeting actions and DAs offer valuable insights about the current conversational context providing valuable cues and features for several related research areas such as speaker addressing and focus of attention modelling, automatic speech recognition and understanding, topic and decision detection

Statistical models for natural scene data

This thesis considers statistical modelling of natural image data. Obtaining advances in this field can have significant impact for both engineering applications, and for the understanding of the human visual system. Several recent advances in natural image modelling have been obtained with the use of unsupervised feature learning. We consider a class of such models, restricted Boltzmann machines (RBMs), used in many recent state-of-the-art image models. We develop extensions of these stochastic artificial neural networks, and use them as a basis for building more effective image models, and tools for computational vision. We first develop a novel framework for obtaining Boltzmann machines, in which the hidden unit activations co-transform with transformed input stimuli in a stable and predictable way throughout the network. We define such models to be transformation equivariant. Such properties have been shown useful for computer vision systems, and have been motivational for example in the development of steerable filters, a widely used classical feature extraction technique. Translation equivariant feature sharing has been the standard method for scaling image models beyond patch-sized data to large images. In our framework we extend shallow and deep models to account for other kinds of transformations as well, focusing on in-plane rotations. Motivated by the unsatisfactory results of current generative natural image models, we take a step back, and evaluate whether they are able to model a subclass of the data, natural image textures. This is a necessary subcomponent of any credible model for visual scenes. We assess the performance of a state- of-the-art model of natural images for texture generation, using a dataset and evaluation techniques from in prior work. We also perform a dissection of the model architecture, uncovering the properties important for good performance. Building on this, we develop structured extensions for more complicated data comprised of textures from multiple classes, using the single-texture model architecture as a basis. These models are shown to be able to produce state-of-the-art texture synthesis results quantitatively, and are also effective qualitatively. It is demonstrated empirically that the developed multiple-texture framework provides a means to generate images of differently textured regions, more generic globally varying textures, and can also be used for texture interpolation, where the approach is radically dfferent from the others in the area. Finally we consider visual boundary prediction from natural images. The work aims to improve understanding of Boltzmann machines in the generation of image segment boundaries, and to investigate deep neural network architectures for learning the boundary detection problem. The developed networks (which avoid several hand-crafted model and feature designs commonly used for the problem), produce the fastest reported inference times in the literature, combined with state-of-the-art performance

Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic