Mean-Field Theory of Meta-Learning

We discuss here the mean-field theory for a cellular automata model of meta-learning. The meta-learning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of interacting, learning agents, that acquire and process incoming information using various types, or different versions of machine learning algorithms. The abstract learning space, where all agents are located, is constructed here using a fully connected model that couples all agents with random strength values. The cellular automata network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the meta-learning system using simple majority rule, yet the minority clusters that share opposite classification outcome can be observed in the system. Therefore, the probability of selecting proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are build from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents.Comment: 23 page

TREE-BASED SURVIVAL MODELS AND PRECISION MEDICINE

Random forests have become one of the most popular machine learning tools in recent years. The main advantage of tree- and forest-based models is their nonparametric nature. My dissertation mainly focuses on a particular type of tree and forest model, in which the outcomes are right censored survival data. Censored survival data are frequently seen in biomedical studies when the true clinical outcome may not be directly observable due to early dropout or other reasons. We first carry out a comprehensive analysis of survival random forest and tree models and show the consistency of these popular machine learning models by developing a general theoretical framework. Our results significantly improve the current understanding of such models and this is the first consistency result of tree- and forest-based regression estimator for censored outcomes under high-dimensional settings. In particular, the consistency results are derived through analyzing the splitting rules and establishing an adaptive concentration bound of the variance component, which may also shed light on the theoretical analysis of other random forest models. In the second part, motivated by tree-based survival models, we propose a fiducial approach to provide pointwise and curvewise confidence intervals for the survival functions. On each terminal node, the estimation is essentially a small sample and maybe heavy censoring problem. Most of the asymptotic methods of estimating confidence intervals have coverage problems in many scenarios. The proposed fiducial based pointwise confidence intervals maintain coverage in these situations. Furthermore, the average length of the proposed pointwise confidence intervals is often shorter than the length of competing methods that maintain coverage. In the third topic, we show one application of tree-based survival models in precision medicine. We extend the outcome weighted learning to right censored survival data without requiring either inverse probability of censoring weighting or semi-parametric modeling of the censoring and failure times. To accomplish this, we take advantage of the tree based approach to nonparametrically impute the survival time in two different ways. We also illustrate the proposed method on a phase III clinical trial of non-small cell lung cancer.Doctor of Philosoph

Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation

Focal sources (FS) are believed to be important triggers and a perpetuation mechanism for paroxysmal atrial fibrillation (AF). Detecting FS and determining AF sustainability in atrial tissue can help guide ablation targeting. We hypothesized that sustained rotors during FS-driven episodes indicate an arrhythmogenic substrate for sustained AF, and that non-invasive electrical recordings, like electrocardiograms (ECGs) or body surface potential maps (BSPMs), could be used to detect FS and AF sustainability. Computer simulations were performed on five bi-atrial geometries. FS were induced by pacing at cycle lengths of 120–270 ms from 32 atrial sites and four pulmonary veins. Self-sustained reentrant activities were also initiated around the same 32 atrial sites with inexcitable cores of radii of 0, 0.5 and 1 cm. FS fired for two seconds and then AF inducibility was tested by whether activation was sustained for another second. ECGs and BSPMs were simulated. Equivalent atrial sources were extracted using second-order blind source separation, and their cycle length, periodicity and contribution, were used as features for random forest classifiers. Longer rotor duration during FS-driven episodes indicates higher AF inducibility (area under ROC curve = 0.83). Our method had accuracy of 90.6±1.0% and 90.6±0.6% in detecting FS presence, and 93.1±0.6% and 94.2±1.2% in identifying AF sustainability, and 80.0±6.6% and 61.0±5.2% in determining the atrium of the focal site, from BSPMs and ECGs of five atria. The detection of FS presence and AF sustainability were insensitive to vest placement (±9.6%). On pre-operative BSPMs of 52 paroxysmal AF patients, patients classified with initiator-type FS on a single atrium resulted in improved two-to-three-year AF-free likelihoods (p-value < 0.01, logrank tests). Detection of FS and arrhythmogenic substrate can be performed from ECGs and BSPMs, enabling non-invasive mapping towards mechanism-targeted AF treatment, and malignant ectopic beat detection with likely AF progression

Application of 1H HR-MAS-NMR spectroscopy in spatial tissue metabolic profiling

HR-MAS-NMR of intact tissue biopsies is a well-established method resulting in one NMR spectrum per whole biopsy showing all detectable metabolites at once. The aim of this project was to explore the possibility and usefulness of monitoring specific locations within the biopsy using HR-MAS-NMR. Firstly, the method was applied to a classic toxicology situation. Many drug development compounds fail because of preclinical animal liver toxicity conventionally detected using histology. Usually, only one of the murine liver lobes is used for this and is assumed to be representative of the whole organ. In this work, a metabolic variation across murine liver lobes has been investigated via a set of biopsies across all lobes. Using HR-MAS-NMR spectra analysed by various types of multivariate analysis, no lobe-specific metabolic variation could be found, confirming the general validity of the representative lobe approach. To increase location specificity, a spatially-resolved NMR pulse sequence (slice local- ized spectroscopy (SLS)) was modified and its respective effectiveness was explored. The pulse sequence was first validated using artificially created samples (phantoms), and practical examples were layered fruit separated by paraffin film and milled phantoms produced from materials which were magnetic-susceptibility-matched to the HR-MAS rotor. The HR-MAS SLS sequence was then applied to a mixed mouse renal tissue biopsy, and renal cortex and medulla successfully assigned to individual slices from spatially-resolved spectra using pure cortex and medulla reference HR-MAS-NMR spectra and orthogonal projection to latent structures discriminant analysis (OPLS-DA) to establish metabolic markers differentiating the two. Together, this work shows the potential of HR-MAS-NMR as applied to tissue biopsies. Particularly, spatially-resolved methods hold potential for improved biochemical and mechanistic understanding and the methodology could be expanded to applications in many areas of biomedical relevance.Open Acces

Improving the Clinical Use of Magnetic Resonance Spectroscopy for the Analysis of Brain Tumours using Machine Learning and Novel Post-Processing Methods

Magnetic Resonance Spectroscopy (MRS) provides unique and clinically relevant information for the assessment of several diseases. However, using the currently available tools, MRS processing and analysis is time-consuming and requires profound expert knowledge. For these two reasons, MRS did not gain general acceptance as a mainstream diagnostic technique yet, and the currently available clinical tools have seen little progress during the past years. MRS provides localized chemical information non-invasively, making it a valuable technique for the assessment of various diseases and conditions, namely brain, prostate and breast cancer, and metabolic diseases affecting the brain. In brain cancer, MRS is normally used for: (1.) differentiation between tumors and non-cancerous lesions, (2.) tumor typing and grading, (3.) differentiation between tumor-progression and radiation necrosis, and (4.) identification of tumor infiltration. Despite the value of MRS for these tasks, susceptibility differences associated with tissue-bone and tissue-air interfaces, as well as with the presence of post-operative paramagnetic particles, affect the quality of brain MR spectra and consequently reduce their clinical value. Therefore, the proper quality management of MRS acquisition and processing is essential to achieve unambiguous and reproducible results. In this thesis, special emphasis was placed on this topic. This thesis addresses some of the major problems that limit the use of MRS in brain tumors and focuses on the use of machine learning for the automation of the MRS processing pipeline and for assisting the interpretation of MRS data. Three main topics were investigated: (1.) automatic quality control of MRS data, (2.) identification of spectroscopic patterns characteristic of different tissue-types in brain tumors, and (3.) development of a new approach for the detection of tumor-related changes in GBM using MRSI data. The first topic tackles the problem of MR spectra being frequently affected by signal artifacts that obscure their clinical information content. Manual identification of these artifacts is subjective and is only practically feasible for single-voxel acquisitions and in case the user has an extensive experience with MRS. Therefore, the automatic distinction between data of good or bad quality is an essential step for the automation of MRS processing and routine reporting. The second topic addresses the difficulties that arise while interpreting MRS results: the interpretation requires expert knowledge, which is not available at every site. Consequently, the development of methods that enable the easy comparison of new spectra with known spectroscopic patterns is of utmost importance for clinical applications of MRS. The third and last topic focuses on the use of MRSI information for the detection of tumor-related effects in the periphery of brain tumors. Several research groups have shown that MRSI information enables the detection of tumor infiltration in regions where structural MRI appears normal. However, many of the approaches described in the literature make use of only a very limited amount of the total information contained in each MR spectrum. Thus, a better way to exploit MRSI information should enable an improvement in the detection of tumor borders, and consequently improve the treatment of brain tumor patients. The development of the methods described was made possible by a novel software tool for the combined processing of MRS and MRI: SpectrIm. This tool, which is currently distributed as part of the jMRUI software suite (www.jmrui.eu), is ubiquitous to all of the different methods presented and was one of the main outputs of the doctoral work. Overall, this thesis presents different methods that, when combined, enable the full automation of MRS processing and assist the analysis of MRS data in brain tumors. By allowing clinical users to obtain more information from MRS with less effort, this thesis contributes to the transformation of MRS into an important clinical tool that may be available whenever its information is of relevance for patient management