Events

Cooperation and competition dynamics of business networks are the focus of a 14 January research seminar by visiting academic Dr Sascha Albers, Professor of International Management at the University of Antwerp Management School in Belgium.

The seminar is hosted by Waikato Management School and will be held in room MSB.4.02, starting at 3pm.

Business networks are cooperative entities formed by more than two firms so as to generate competitive advantages for each member. They often operate in dynamic contexts, to which they need to adapt. Some of these dynamics result from other business networks’ actions of adaptation and strategic manoeuvring, constituting forms of competition between such networks.

Three specific forms of such competition exist: (1) competition in network formation, (2) competition in network composition, and (3) competition in network governance.

This seminar will explain each of these network competition forms, present exemplary research results for each and suggests implications for network and corporate management and research.

Dr Albers’ teaching, research, and consulting focuses on strategic management and inter-organisational design problems in international business contexts, particularly in the transportation industry.

He has worked with major organisations such as Deutsche Bahn, Deutsche Telekom, DHL, DKV Mobility Services, Lufthansa, and Siemens on issues of strategy formulation, alliance governance and internationalisation strategy.

His research has been published in leading international management and logistics journals, such as the Journal of Management and the International Journal of Physical Distribution and Logistics Management.

Professor Albers is a research fellow at the University of Cologne and has been a visiting lecturer at various business schools in the US, Europe, Australia, and New Zealand.

More information.

Speaker: Jonathan Rubin, PhD is Senior Scientist at Philips Research North America and a Research Affiliate of the ALFA Group at MIT's Computer Science and Artificial Intelligence Laboratory

Abstract

In this talk, I present two applications of training deep learning models applied to medical datasets:

1. Densely Connected Convolutional Networks for Detection of Atrial Fibrillation from Short Single-Lead ECG Recordings

The first work processes and analyzes electrocardiogram (ECG) waveforms to detect Atrial Fibrillation (AF). AF is the most common sustained cardiac arrhythmia and is associated with an increased risk of stroke and mortality. Timely diagnosis of AF is clinically desirable because interventions can be applied to prevent the loss of sinus rhythm. In this work, we developed deep learning based algorithms for classification of normal sinus rhythm (NSR), AF, other rhythms (O) and noise, using short-segment, single-channel ECG waveforms as input. We evaluated our models at the 2017 PhysioNet / Computing in Cardiology Challenge, where our entry achieved the third highest score (F1 = 0.82) out of 80 teams in the final phase of the challenge.

2. Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Conditional Generative Adversarial Neural Networks, Pyramid Pooling and Focal Loss

The second work uses fully convolutional neural networks (FCN) for segmenting ischemic stroke lesions in Computed Tomography (CT) perfusion images. Treatment of stroke is time sensitive and automatic segmentation methods present the possibility of accurately identifying lesions and improving treatment planning. Our FCN architecture makes use of pyramid pooling to provide global and local contextual information. To learn the varying shapes of the lesions, we train our network using focal loss, a loss function designed for the network to focus on learning the more difficult samples. We evaluated our approach at the 2018 Ischemic Stroke Lesion Segmentation (ISLES) challenge where it ranked among the top performers at the challenge conclusion. In addition, I will present recent work that improves the performance of the FCN model, using synthesized inputs created with a conditional generative adversarial network.

More information.