Segmentation of colon glands by object graphs

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.Thesis (Master's) -- Bilkent University, 2008.Includes bibliographical references leaves 72-79.Histopathological examination is the most frequently used technique for clinical diagnosis of a large group of diseases including cancer. In order to reduce the observer variability and the manual effort involving in this visual examination, many computational methods have been proposed. These methods represent a tissue with a set of mathematical features and use these features in further analysis of the biopsy. For the tissue types that contain glandular structures, one of these analyses is to examine the changes in these glandular structures. For such analyses, the very first step is to segment the tissue into its glands. In this thesis, we present an object-based method for the segmentation of colon glands. In this method, we propose to decompose the image into a set of primitive objects and use the spatial distribution of these objects to determine the locations of glands. In the proposed method, pixels are first clustered into different histological structures with respect to their color intensities. Then, the clustered image is decomposed into a set of circular primitive objects (white objects for luminal regions and black objects for nuclear regions) and a graph is constructed on these primitive objects to quantify their spatial distribution. Next, the features are extracted from this graph and these features are used to determine the seed points of gland candidates. Starting from these seed points, the inner glandular regions are grown considering the locations of black objects. Finally, false glands are eliminated based on another set of features extracted from the identified inner regions and exact boundaries of the remaining true glands are determined considering the black objects that are located near the inner glandular regions. Our experiments on the images of colon biopsies have demonstrated that our proposed method leads to high sensitivity, specificity, and accuracy rates.and that it greatly improves the performance of the previous pixel-based gland segmentation algorithms. Our experiments have also shown that the object-based structure of the method provides tolerance to artifacts resulting from variances in biopsy staining and sectioning procedures. This proposed method offers an infrastructure for further analysis of glands for the purpose of automated cancer diagnosis and grading.Kandemir, MelihM.S

Learning Mental States from Biosignals

As computing technology evolves, users perform more complex tasks with computers. Hence, users expect from user interfaces to be more proactive than reactive. A proactive interface should anticipate the user’s intentions and take the right action without requiring a user command. The crucial first step for such an interface is to infer the user’s mental state, which gives important cues about user intentions. This thesis consists of several case studies on inferring mental states of computer users.  Biosensing technology provides a variety of hardware tools for measuring several aspects of human physiology, which is correlated with emotions and mental processes. However, signals gathered with biosensors are notoriously noisy. The mainstream approach to overcome this noise is either to increase the signal precision by expensive and stationary sensors or to control the experiment setups more heavily. Both of these solutions undermine the usability of the developed methods in real-life user interfaces. In this thesis, machine learning is used as an alternative strategy for handling the biosignal noise in mental state inference. Computer users have been monitored under loosely controlled experiment setups by cheap and inaccurate biosensors, and novel machine learning models that infer mental states such as affective state, mental workload, relevance of a real-world object, and auditory attention are built. The methodological contributions of the thesis are mainly on multi-view learning and multitask learning. Multi-view learning is used for integrating signals of multiple biosensors and the stimuli. Multitask learning is used for inferring multiple mental states at once, and for exploiting the inter-subject similarities for higher prediction accuracy. A novel multitask learning algorithm that transfers knowledge across multi-view learning tasks is introduced. Another novelty is a Bayesian factor analyzer with a time-dependent latent space that captures the dynamic nature of biosignals better than methods that assume independent samples. The overall outcome of the thesis is that it is feasible to predict mental states from unobtrusive biosensors with reasonable accuracy using state-of-the-art machine learning models

BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits

We propose a novel Bayesian-Optimistic Frequentist Upper Confidence Bound (BOF-UCB) algorithm for stochastic contextual linear bandits in non-stationary environments. This unique combination of Bayesian and frequentist principles enhances adaptability and performance in dynamic settings. The BOF-UCB algorithm utilizes sequential Bayesian updates to infer the posterior distribution of the unknown regression parameter, and subsequently employs a frequentist approach to compute the Upper Confidence Bound (UCB) by maximizing the expected reward over the posterior distribution. We provide theoretical guarantees of BOF-UCB's performance and demonstrate its effectiveness in balancing exploration and exploitation on synthetic datasets and classical control tasks in a reinforcement learning setting. Our results show that BOF-UCB outperforms existing methods, making it a promising solution for sequential decision-making in non-stationary environments

Continual Learning of Multi-modal Dynamics with External Memory

We study the problem of fitting a model to a dynamical environment when new modes of behavior emerge sequentially. The learning model is aware when a new mode appears, but it does not have access to the true modes of individual training sequences. We devise a novel continual learning method that maintains a descriptor of the mode of an encountered sequence in a neural episodic memory. We employ a Dirichlet Process prior on the attention weights of the memory to foster efficient storage of the mode descriptors. Our method performs continual learning by transferring knowledge across tasks by retrieving the descriptors of similar modes of past tasks to the mode of a current sequence and feeding this descriptor into its transition kernel as control input. We observe the continual learning performance of our method to compare favorably to the mainstream parameter transfer approach

PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison

PAC-Bayes has recently re-emerged as an effective theory with which one can derive principled learning algorithms with tight performance guarantees. However, applications of PAC-Bayes to bandit problems are relatively rare, which is a great misfortune. Many decision-making problems in healthcare, finance and natural sciences can be modelled as bandit problems. In many of these applications, principled algorithms with strong performance guarantees would be very much appreciated. This survey provides an overview of PAC-Bayes bounds for bandit problems and an experimental comparison of these bounds. On the one hand, we found that PAC-Bayes bounds are a useful tool for designing offline bandit algorithms with performance guarantees. In our experiments, a PAC-Bayesian offline contextual bandit algorithm was able to learn randomised neural network polices with competitive expected reward and non-vacuous performance guarantees. On the other hand, the PAC-Bayesian online bandit algorithms that we tested had loose cumulative regret bounds. We conclude by discussing some topics for future work on PAC-Bayesian bandit algorithms.Comment: 32 pages, 8 figure