Graph Inference with Applications to Low-Resource Audio Search and Indexing

The task of query-by-example search is to retrieve, from among a collection of data, the observations most similar to a given query. A common approach to this problem is based on viewing the data as vertices in a graph in which edge weights reflect similarities between observations. Errors arise in this graph-based framework both from errors in measuring these similarities and from approximations required for fast retrieval. In this thesis, we use tools from graph inference to analyze and control the sources of these errors. We establish novel theoretical results related to representation learning and to vertex nomination, and use these results to control the effects of model misspecification, noisy similarity measurement and approximation error on search accuracy. We present a state-of-the-art system for query-by-example audio search in the context of low-resource speech recognition, which also serves as an illustrative example and testbed for applying our theoretical results

Data-driven shape analysis and processing

Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing

Novel Deep Learning Techniques For Computer Vision and Structure Health Monitoring

This thesis proposes novel techniques in building a generic framework for both the regression and classification tasks in vastly different applications domains such as computer vision and civil engineering. Many frameworks have been proposed and combined into a complex deep network design to provide a complete solution to a wide variety of problems. The experiment results demonstrate significant improvements of all the proposed techniques towards accuracy and efficiency

Deep Learning for Detection and Segmentation in High-Content Microscopy Images

High-content microscopy led to many advances in biology and medicine. This fast emerging technology is transforming cell biology into a big data driven science. Computer vision methods are used to automate the analysis of microscopy image data. In recent years, deep learning became popular and had major success in computer vision. Most of the available methods are developed to process natural images. Compared to natural images, microscopy images pose domain specific challenges such as small training datasets, clustered objects, and class imbalance. In this thesis, new deep learning methods for object detection and cell segmentation in microscopy images are introduced. For particle detection in fluorescence microscopy images, a deep learning method based on a domain-adapted Deconvolution Network is presented. In addition, a method for mitotic cell detection in heterogeneous histopathology images is proposed, which combines a deep residual network with Hough voting. The method is used for grading of whole-slide histology images of breast carcinoma. Moreover, a method for both particle detection and cell detection based on object centroids is introduced, which is trainable end-to-end. It comprises a novel Centroid Proposal Network, a layer for ensembling detection hypotheses over image scales and anchors, an anchor regularization scheme which favours prior anchors over regressed locations, and an improved algorithm for Non-Maximum Suppression. Furthermore, a novel loss function based on Normalized Mutual Information is proposed which can cope with strong class imbalance and is derived within a Bayesian framework. For cell segmentation, a deep neural network with increased receptive field to capture rich semantic information is introduced. Moreover, a deep neural network which combines both paradigms of multi-scale feature aggregation of Convolutional Neural Networks and iterative refinement of Recurrent Neural Networks is proposed. To increase the robustness of the training and improve segmentation, a novel focal loss function is presented. In addition, a framework for black-box hyperparameter optimization for biomedical image analysis pipelines is proposed. The framework has a modular architecture that separates hyperparameter sampling and hyperparameter optimization. A visualization of the loss function based on infimum projections is suggested to obtain further insights into the optimization problem. Also, a transfer learning approach is presented, which uses only one color channel for pre-training and performs fine-tuning on more color channels. Furthermore, an approach for unsupervised domain adaptation for histopathological slides is presented. Finally, Galaxy Image Analysis is presented, a platform for web-based microscopy image analysis. Galaxy Image Analysis workflows for cell segmentation in cell cultures, particle detection in mice brain tissue, and MALDI/H&E image registration have been developed. The proposed methods were applied to challenging synthetic as well as real microscopy image data from various microscopy modalities. It turned out that the proposed methods yield state-of-the-art or improved results. The methods were benchmarked in international image analysis challenges and used in various cooperation projects with biomedical researchers

Deep learning-based diagnostic system for malignant liver detection

Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent, accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification. In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms. However, such traditional methods could immensely affect the structural properties of processed images with inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use. To address these limitations, I propose novel methodologies in this dissertation. First, I modified a generative adversarial network to perform deblurring and contrast adjustment on computed tomography (CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver detection. The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods. The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification. A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions. Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants. In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore, the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis

Sparse convex optimization methods for machine learning

Diss., Eidgenössische Technische Hochschule ETH Zürich, Nr. 20013, 201

Robust and Scalable Data Representation and Analysis Leveraging Isometric Transformations and Sparsity

The main focus of this doctoral thesis is to study the problem of robust and scalable data representation and analysis. The success of any machine learning and signal processing framework relies on how the data is represented and analyzed. Thus, in this work, we focus on three closely related problems: (i) supervised representation learning, (ii) unsupervised representation learning, and (iii) fault tolerant data analysis. For the first task, we put forward new theoretical results on why a certain family of neural networks can become extremely deep and how we can improve this scalability property in a mathematically sound manner. We further investigate how we can employ them to generate data representations that are robust to outliers and to retrieve representative subsets of huge datasets. For the second task, we will discuss two different methods, namely compressive sensing (CS) and nonnegative matrix factorization (NMF). We show that we can employ prior knowledge, such as slow variation in time, to introduce an unsupervised learning component to the traditional CS framework and to learn better compressed representations. Furthermore, we show that prior knowledge and sparsity constraint can be used in the context of NMF, not to find sparse hidden factors, but to enforce other structures, such as piece-wise continuity. Finally, for the third task, we investigate how a data analysis framework can become robust to faulty data and faulty data processors. We employ Bayesian inference and propose a scheme that can solve the CS recovery problem in an asynchronous parallel manner. Furthermore, we show how sparsity can be used to make an optimization problem robust to faulty data measurements. The methods investigated in this work have applications in different practical problems such as resource allocation in wireless networks, source localization, image/video classification, and search engines. A detailed discussion of these practical applications will be presented for each method

Applications of nonlinear approximation for problems in learning theory and applied mathematics

A major pillar of approximation theory in establishing the ability of one class of functions to be represented by another. Establishing such a relationship often leads to efficient numerical approximation methods. In this work, several expressibility theorems are established and several novel numerical approximation techniques are also presented. Not only are these novel methods supported by the presented theory, but also, provided numerical experiments show that these novel methods may be applied to a wide range of applications from image compression to the solutions of high-dimensional PDE