A robust framework for medical image segmentation through adaptable class-specific representation

Medical image segmentation is an increasingly important component in virtual pathology, diagnostic imaging and computer-assisted surgery. Better hardware for image acquisition and a variety of advanced visualisation methods have paved the way for the development of computer based tools for medical image analysis and interpretation. The routine use of medical imaging scans of multiple modalities has been growing over the last decades and data sets such as the Visible Human Project have introduced a new modality in the form of colour cryo section data. These developments have given rise to an increasing need for better automatic and semiautomatic segmentation methods. The work presented in this thesis concerns the development of a new framework for robust semi-automatic segmentation of medical imaging data of multiple modalities. Following the specification of a set of conceptual and technical requirements, the framework known as ACSR (Adaptable Class-Specific Representation) is developed in the first case for 2D colour cryo section segmentation. This is achieved through the development of a novel algorithm for adaptable class-specific sampling of point neighbourhoods, known as the PGA (Path Growing Algorithm), combined with Learning Vector Quantization. The framework is extended to accommodate 3D volume segmentation of cryo section data and subsequently segmentation of single and multi-channel greyscale MRl data. For the latter the issues of inhomogeneity and noise are specifically addressed. Evaluation is based on comparison with previously published results on standard simulated and real data sets, using visual presentation, ground truth comparison and human observer experiments. ACSR provides the user with a simple and intuitive visual initialisation process followed by a fully automatic segmentation. Results on both cryo section and MRI data compare favourably to existing methods, demonstrating robustness both to common artefacts and multiple user initialisations. Further developments into specific clinical applications are discussed in the future work section

Acceptable Lies in Contract Negotiations

It is well established that lying is a widespread phenomenon in business-to-business (“B2B”) contract negotiations. Some of the most prominent lies may be those about the subject matter of the contract. However, negotiators also frequently lie about other aspects like offers from other potential buyers or sellers, the availability of their product, the legal situation regarding contractual aspects, as well as their emotions and preferences

On the Design, Implementation and Application of Novel Multi-disciplinary Techniques for explaining Artificial Intelligence Models

284 p.Artificial Intelligence is a non-stopping field of research that has experienced some incredible growth lastdecades. Some of the reasons for this apparently exponential growth are the improvements incomputational power, sensing capabilities and data storage which results in a huge increment on dataavailability. However, this growth has been mostly led by a performance-based mindset that has pushedmodels towards a black-box nature. The performance prowess of these methods along with the risingdemand for their implementation has triggered the birth of a new research field. Explainable ArtificialIntelligence. As any new field, XAI falls short in cohesiveness. Added the consequences of dealing withconcepts that are not from natural sciences (explanations) the tumultuous scene is palpable. This thesiscontributes to the field from two different perspectives. A theoretical one and a practical one. The formeris based on a profound literature review that resulted in two main contributions: 1) the proposition of anew definition for Explainable Artificial Intelligence and 2) the creation of a new taxonomy for the field.The latter is composed of two XAI frameworks that accommodate in some of the raging gaps found field,namely: 1) XAI framework for Echo State Networks and 2) XAI framework for the generation ofcounterfactual. The first accounts for the gap concerning Randomized neural networks since they havenever been considered within the field of XAI. Unfortunately, choosing the right parameters to initializethese reservoirs falls a bit on the side of luck and past experience of the scientist and less on that of soundreasoning. The current approach for assessing whether a reservoir is suited for a particular task is toobserve if it yields accurate results, either by handcrafting the values of the reservoir parameters or byautomating their configuration via an external optimizer. All in all, this poses tough questions to addresswhen developing an ESN for a certain application, since knowing whether the created structure is optimalfor the problem at hand is not possible without actually training it. However, some of the main concernsfor not pursuing their application is related to the mistrust generated by their black-box" nature. Thesecond presents a new paradigm to treat counterfactual generation. Among the alternatives to reach auniversal understanding of model explanations, counterfactual examples is arguably the one that bestconforms to human understanding principles when faced with unknown phenomena. Indeed, discerningwhat would happen should the initial conditions differ in a plausible fashion is a mechanism oftenadopted by human when attempting at understanding any unknown. The search for counterfactualsproposed in this thesis is governed by three different objectives. Opposed to the classical approach inwhich counterfactuals are just generated following a minimum distance approach of some type, thisframework allows for an in-depth analysis of a target model by means of counterfactuals responding to:Adversarial Power, Plausibility and Change Intensity

Swarm-Organized Topographic Mapping

Topographieerhaltende Abbildungen versuchen, hochdimensionale oder komplexe Datenbestände auf einen niederdimensionalen Ausgaberaum abzubilden, wobei die Topographie der Daten hinreichend gut wiedergegeben werden soll. Die Qualität solcher Abbildung hängt gewöhnlich vom eingesetzten Nachbarschaftskonzept des konstruierenden Algorithmus ab. Die Schwarm-Organisierte Projektion ermöglicht eine Lösung dieses Parametrisierungsproblems durch die Verwendung von Techniken der Schwarmintelligenz. Die praktische Verwendbarkeit dieser Methodik wurde durch zwei Anwendungen auf dem Feld der Molekularbiologie sowie der Finanzanalytik demonstriert

Seagrass and Seaweed Detection Approaches Using Remote Sensing and Google Earth Engine: A comparative Analysis of Different Machine Learning Techniques

Seagrasses and seaweed habitats contribute to crucial ecological services globally, from capturing carbon dioxide and supporting 20% of the world’s largest fisheries to sustaining the small, but many coastal communities [1]. Across the globe, an alarming decline in their wild distribution has been recorded, attributed to climate change and direct pollution [2]. Current estimates of how much the loss is are uncertain and mapping and monitoring efforts are costly, data-intensive, and lack scalability. Thus, freely available data and software in remote sensing, coupled with Machine Learning (ML) are deemed important means to leverage existing mapping of seagrasses and seaweed spatial distribution [3, 4]. This thesis explored a free and scalable workflow by comparing three different ML techniques mainly on Overall Accuracy (OA) and Tau(e) in classifying seagrass, seaweed, and water. These are supervised, unsupervised, and semi-supervised learning (SSL) which used data from the satellite, Sentinel-2 Level-2A, applied to a novel area of study, from Biddeford Pool to Small Point at the Coast of Maine, United States of America. Results showed that the SSL achieved the highest OA of 76% and Tau(e) = 0.72 on the hard test, in line with previous work. To the best of the author’s knowledge, this work contributes to the field of science by being the first in its field to use the geospatial analysis package ’geemap’, along with the software Google Earth Engine, and SSL for classifying seagrass and seaweeds. Through demonstration, this work shows the potential of free data in remote sensing, leveraged by ML to aid community monitoring in the environmental management of seagrass and seaweed. The results here can be considered as a starting point for further exploring the SSL paired with freely available data and community monitoring to lower costs, handle data scarcity, and scale up in the field of aquatic vegetation mapping and monitoring.Seagrasses and seaweed habitats contribute to crucial ecological services globally, from capturing carbon dioxide and supporting 20% of the world’s largest fisheries to sustaining the small, but many coastal communities [1]. Across the globe, an alarming decline in their wild distribution has been recorded, attributed to climate change and direct pollution [2]. Current estimates of how much the loss is are uncertain and mapping and monitoring efforts are costly, data-intensive, and lack scalability. Thus, freely available data and software in remote sensing, coupled with Machine Learning (ML) are deemed important means to leverage existing mapping of seagrasses and seaweed spatial distribution [3, 4]. This thesis explored a free and scalable workflow by comparing three different ML techniques mainly on Overall Accuracy (OA) and Tau(e) in classifying seagrass, seaweed, and water. These are supervised, unsupervised, and semi-supervised learning (SSL) which used data from the satellite, Sentinel-2 Level-2A, applied to a novel area of study, from Biddeford Pool to Small Point at the Coast of Maine, United States of America. Results showed that the SSL achieved the highest OA of 76% and Tau(e) = 0.72 on the hard test, in line with previous work. To the best of the author’s knowledge, this work contributes to the field of science by being the first in its field to use the geospatial analysis package ’geemap’, along with the software Google Earth Engine, and SSL for classifying seagrass and seaweeds. Through demonstration, this work shows the potential of free data in remote sensing, leveraged by ML to aid community monitoring in the environmental management of seagrass and seaweed. The results here can be considered as a starting point for further exploring the SSL paired with freely available data and community monitoring to lower costs, handle data scarcity, and scale up in the field of aquatic vegetation mapping and monitoring

Revisiting the Mind- Body Paradox: Can Brain Functioning Explain Moral Reasoning?

With this paper, I attempt to explore possible neural correlates of morality. We define morality as the one part, structure, or process of the brain that could be linked to an innate ability to understand and determine right versus wrong. An understanding of right of right and wrong can provide us with a sense of guilt and empathy for an action or another person. Right and wrong will be defined through a primarily Judeo-Christian perspective, as it was the principle respondent among our questionnaire. There is a possibility for differences among other religions. For that reason, we expect the neural correlate to be flexible enough to lead to variations. Mirror neurons, or neurons with the ability to excite while watching and executing an action, will be the neural correlate I will explore. Using a combination of Jaynes theory of consciousness, Hawkins hierarchical temporal memory, and a pattern recognition associative network, I will recreate a mirror neuron network, which could represent a learning pattern which develops to classify actions as “right” or “wrong” (Jaynes, 1990, Hawkins 2005)

Five sources of bias in natural language processing

Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much of the recent work has focused on describing bias and providing an overview of bias in a larger context. Here, we provide a simple, actionable summary of this recent work. We outline five sources where bias can occur in NLP systems: (1) the data, (2) the annotation process, (3) the input representations, (4) the models, and finally (5) the research design (or how we conceptualize our research). We explore each of the bias sources in detail in this article, including examples and links to related work, as well as potential counter-measures