Applications of deep learning, optimization, and statistics in medical research

In the ever-evolving landscape of healthcare, the demand for sophisticated and efficient diagnostic tools has intensified. Medical imaging, serving as a cornerstone in disease detection and characterization, has witnessed a transformative shift with the advent of ar- tificial intelligence. In this thesis we explore the usage of both traditional image processing methods and advanced deep learning techniques to address medical questions. This thesis starts with a short overview of mathematical concepts, definitions, and proofs which we will use in later chapters. In Chapter 3, three image processing techniques are introduced, namely color decon- volution, background subtraction, and thresholding. Within Section 3.3, we propose a novel algorithm designed to efficiently compute multilevel thresholds. Depending on the input parameters, this algorithm is multiple orders of magnitude faster than currently used implementations. We achieve this by using an improving moves algorithm to find a local maximum and then repeat this process with different initial values to increase the likelihood of terminating in the global maximum. We show that this approach will find the best threshold in almost all test images, for both medical and images from the ImageNet (Deng et al. (2009)) dataset. In Chapter 4 we use a deep neural network to predict whether patients will develop a distant metastasis in five years after treatment based on images of the invasion front of their tumor. The early identification of metastatic risk can be a valuable tool for guiding treatment decisions, allowing for the implementation of more aggressive protocols when deemed necessary. The model takes binary images of tumors as input to emphasize the architecture of the tumor. These images were prepared from stained histological images for the invasion front of the tumor, which were subsequently transformed into binary black and white images. Based on the output of the neural network each patient is assigned to a high or low risk group. A drawback associated with the utilization of deep learning models is their tendency to function as black boxes, often making it challenging to decipher the factors influencing a particular prediction. Following the fitting and evaluation of the neural network model in Section 4.1, we employed two methodologies to delve into the reasoning behind the model’s predictions in Section 4.2. Firstly, we generated heatmaps to visually represent the areas of the input image exerting the most influence on the prediction. Subsequently, we crafted synthetic images with specific features to examine how these features, along with their intensity, impacted the prediction of the model. Our initial approach to create synthetic images involved using noise to generate random images. This enabled us to observe the influence of noise parameters on the model’s predictions. Further exploration involved modifying tumor borders of images previously used in the evaluation of the model and analyzing the effect of these alterations. In Chapter 5 we use deterministic image manipulation algorithms, as introduced in Chapter 3, to calculate a score for the expression of focal adhesion kinase (FAK) in tissue using microscope images. FAK expression is often used as a diagnostic marker, but is mostly evaluated subjectively by the individual pathologist. Our approach involves approximating the proportional stain concentration within lesions in comparison to the surrounding tissue, leveraging the Beer–Lambert Law (Beer (1852)). In Chapter 6, we present four supplementary studies, each of which culminated in publications resulting from collaborative efforts with research groups at the Augsburg University Hospital. Here the majority of the work was performed by the project partners at the Hospital, who performed the studies and aggregated the data. My involvement was planning and performing the statistical evaluation of said data. In Section 6.1, we present two studies scrutinizing the disparities in lymphocyte cell counts between distinct patient groups and a healthy control cohort. The initial study focuses on patients afflicted with COVID-19, while the subsequent investigation centers on patients diagnosed with colorectal cancer. In Section 6.2 two studies are outlined that investigate the potential advantages that patients can derive from the implementation of virtual reality-based interventions during their hospitalization and recovery periods

Planning Support for the Design of Quality Control Strategies in Global Production Networks

The increasing globalization forces manufacturing companies to organize their production in global networks, which include company internal sites as well as locations of external partners and suppliers. Each site in this network has an assigned strategic role according to the specific location factors, i.e. qualification level of employees or available process technology, and the defined specialization of each site, i.e. regarding served market, final product or realized processes. This role defines an individual target system that considers at least the dimensions cost, quality and time. Each site acts autonomously according to the target system. Since it is crucial for the success of the company to ensure the demanded quality of the final product with minimal cumulated quality costs and lead times, the quality control strategy for the production network has to be designed according to the target systems of the individual sites. The presented article describes an approach, which enables globally operating companies to efficiently plan their efforts for their quality control measures in their respective production network taking the specific site roles into account. In a first step, a value-stream-based methodology is presented, which visualizes quality characteristics as well as related quality inspections in the production process chain and which identifies potentials in the quality control strategy across locations. In a second step a simulation approach is used to evaluate the effects of different quality measures considering dynamic influencing factors and individual target systems, so that the optimal quality control strategy for the production network can be identified

Die Veränderung der Softwareerstellung durch Open Source

Zu Beginn der Computer-Ära war Software ein Gut, welches hauptsächlich als Zugabe beim Erwerb eines Computersystems gesehen wurde. Es existierten keine Softwarefirmen im heutigen Sinne, sondern die Software war ein Bestandteil der von den Hardwareherstellern angebotenen Produkte. Viele zusätzliche Programme wurden zu dieser Zeit von Anwendern für ihre eigenen Bedürfnisse geschrieben und oftmals als public domain zur Verfügung gestellt. Mitte der '70er Jahre trat hier eine Wende hin zu kommerziell vertriebener Software ein. Seit der Gründung der Free Software Foundation (FSF), 1985, entstand eine organisierte Gegenbewegung Hier haben auch die heute bekannte GNU/General Public License, unter der auch Linux vertrieben wird, sowie das gesamte GNU-Projekt ihre gemeinsamen Wurzeln. Mit Gründung der Open Source Initiative6 (OSI) 1998 und der damit verbundenen Veröffentlichung der ersten Version der Open Source Definition (OSD) wurde der Begriff „Open Source“ (OS), wie wir ihn heute kennen einer breiten Öffentlichkeit zugänglich gemacht. Die OSD (mittlerweile in der Version 1.9) ist selbst keine Lizenz sondern eine zehn Punkte umfassende Bedingung, die eine Lizenz erfüllen muss, um als OS Lizenz zu gelten. Hierbei stehen vor allem die freie Verfügbarkeit des Quelltextes und die uneingeschränkte Weitergabemöglichkeit der Software an sich sowie des Quelltextes im Besonderen im Vordergrund. Außerdem müssen eine Veränderung des Quelltextes und die Weitergabe der veränderten Quellen uneingeschränkt gestattet sein. Der Unterschied zwischen den Ansichten der FSF und der Open Source Bewegung (OSI) finden sich in deren unterschiedlichen Betrachtungsweisen der Softwarewelt. Für die Open Source Bewegung ist die Frage, ob eine Software quelloffen (open source) sein sollte, eine rein praktische und keine ethische. Eine nicht open source stehende Software stellt im Sinne der Open Source Bewegung lediglich eine suboptimale Lösung dar. Die OSI sieht ihre Aufgabe vor allem in der Verwaltung und dem Marketing der OSD. Für die FSF dagegen, stellt sie hauptsächlich ein soziales Problem dar. Der ursprüngliche Entstehungsweg eines Open Source Projekts ging bisher von einem einzelnen Entwickler aus, der mit einem bestehenden Produkt unzufrieden war oder für ein bestimmtes Problem keine passende Lösung fand. Eine „strategische Ausrichtung“ von OS war in diesem Kontext nur schwer möglich. Diese Begrenzung von Open Source beginnt sich heute allerdings immer weiter aufzulösen. Viele Unternehmen sehen in Open Source mittlerweile eine alternative Möglichkeit und Chance, die es sich lohnt zu fördern und voranzutreiben. Im Weiteren wird diskutiert, inwiefern Methoden der Open Source Softwareentwicklung (OSSE) auch auf Unternehmensebene, d.h. in einem kommerziellen Umfeld, zum Einsatz kommen können. Es geht nicht darum, eine qualitative, produktbezogene Untersuchung von Open Source Software (OSS) im Vergleich zu proprietärer Software durchzuführen, sondern den möglichen Nutzen von OSSE für Unternehmen, die Software herstellen, zu analysieren

A Flexible Simulation Support for Production Planning and Control in Small and Medium Enterprises

For efficient, effective and economical production operation management in a manufacturing unit of an organization, it is essential to integrate the production planning and control system into an enterprise resource planning. Today\u27s planning systems suffer from a low range in planning data which results in unrealistic delivery times. One of the root causes is that production is influenced by uncertainties such as machine breakdowns, quality issues and the scheduling principle. Hence, it is necessary to model and simulate production planning and controls (PPC) with information dynamics in order to analyze the risks that are caused by multiple uncertainties. In this context, a new approach to simulate PPC systems is exposed in this paper, which aims at visualizing the production process and comparing key performance indicators (KPIs) as well as optimizing PPC parameters under different uncertainties in order to deal with potential risk consuming time and effort. Firstly, a production system simulation is created to quickly obtain different KPIs (e.g. on time delivery rate, quality, cost, machine utilization, WIP) under different uncertainties, which can be flexibly set by users. Secondly, an optimization experiment is conducted to optimize the parameters of PPC with regard to the different KPIs. An industrial case study is used to demonstrate the applicability and the validity of the proposed approach

Risk factor analysis for fast track protocol failure

Background: The introduction of fast-track treatment procedures following cardiac surgery has significantly shortened hospitalisation times in intensive care units (ICU). Readmission to intensive care units is generally considered a negative quality criterion. The aim of this retrospective study is to statistically analyse risk factors and predictors for re-admission to the ICU after a fast-track patient management program. Methods: 229 operated patients (67 ± 11 years, 75% male, BMI 27 ± 3, 6/2010-5/2011) with use of extracorporeal circulation (70 ± 31 min aortic crossclamping, CABG 62%) were selected for a preoperative fast-track procedure (transfer on the day of surgery to an intermediate care (IMC) unit, stable circulatory conditions, extubated). A uni- and multivariate analysis were performed to identify independent predictors for re-admission to the ICU. Results: Over the 11-month study period, 36% of all preoperatively declared fast-track patients could not be transferred to an IMC unit on the day of surgery (n = 77) or had to be readmitted to the ICU after the first postoperative day (n = 4). Readmission or ICU stay signifies a dramatic worsening of the patient outcome (mortality 0/10%, mean hospital stay 10.3 ± 2.5/16.5 ± 16.3, mean transfusion rate 1.4 ± 1,7/5.3 ± 9.1). Predicators for failure of the fast-track procedure are a preoperative ASA class > 3, NYHA class > III and an operation time >267 min ± 74. The significant risk factors for a major postoperative event (= low cardiac output and/or mortality and/or renal failure and/or re-thoracotomy and/or septic shock and/or wound healing disturbances and/or stroke) are a poor EF (OR 2.7 CI 95% 0.98-7.6) and the described ICU readmission (OR 0.14 CI95% 0.05-0.36). Conclusion: Re-admission to the ICU or failure to transfer patients to the IMC is associated with a high loss of patient outcome. The ASA > 3, NYHA class > 3 and operation time >267 minutes are independent predictors of fast track protocol failure

Antitrust, the Gig Economy, and Labor Market Power

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear

Developing Genetic Algorithms and Mixed Integer Linear Programs for Finding Optimal Strategies for a Student’s “Sports” Activity

An important advantage of genetic algorithms (GAs) are their ease of use, their wide applicability, and their good performance for a wide range of different problems. GAs are able to find good solutions for many problems even if the problem is complicated and its properties are not well known. In contrast, classical optimization approaches like linear programming or mixed integer linear programs (MILP) can only be applied to restricted types of problems as non-linearities of a problem that occur in many real-world applications can not appropriately modeled. This paper illustrates for an entertaining student “sports” game that GAs can easily be adapted to a problem where only limited knowledge about its properties and complexity are available and are able to solve the problem easily. Modeling the problem as a MILP and trying to solve it by using a standard MILP solver reveals that it is not solvable within reasonable time whereas GAs can solve it in a few seconds. The game studied is known to students as the so-called “beer-run”. There are different teams that have to walk a certain distance and to carry a case of beer. When reaching the goal all beer must have been consumed by the group and the winner of the game is the fastest team. The goal of optimization algorithms is to determine a strategy that minimizes the time necessary to reach the goal. This problem was chosen as it is not well studied and allows to demonstrate the advantages of using metaheuristics like GAs in comparison to standard optimization methods like MILP solvers for problems of unknown structure and complexity

Probabilistic Routing for On-Street Parking Search

An estimated 30% of urban traffic is caused by search for parking spots [Shoup, 2005]. Suggesting routes along highly probable parking spots could reduce traffic. In this paper, we formalize parking search as a probabilistic problem on a road graph and show that it is NP-complete. We explore heuristics that optimize for the driving duration and the walking distance to the destination. Routes are constrained to reach a certain probability threshold of finding a spot. Empirically estimated probabilities of successful parking attempts are provided by TomTom on a per-street basis. We release these probabilities as a dataset of about 80,000 roads covering the Berlin area. This allows to evaluate parking search algorithms on a real road network with realistic probabilities for the first time. However, for many other areas, parking probabilities are not openly available. Because they are effortful to collect, we propose an algorithm that relies on conventional road attributes only. Our experiments show that this algorithm comes close to the baseline by a factor of 1.3 in our cost measure. This leads to the conclusion that conventional road attributes may be sufficient to compute reasonably good parking search routes