Surgical Data Science - from Concepts toward Clinical Translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process

Learning Compositional Visual Concepts with Mutual Consistency

Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data. For instance, we may learn concepts of changing illumination, geometry or albedo of a scene, and try to recombine them to generate physically meaningful, but unseen data for training and testing. In practice however we often do not have samples from the joint concept space available: We may have data on illumination change in one data set and on geometric change in another one without complete overlap. We pose the following question: How can we learn two or more concepts jointly from different data sets with mutual consistency where we do not have samples from the full joint space? We present a novel answer in this paper based on cyclic consistency over multiple concepts, represented individually by generative adversarial networks (GANs). Our method, ConceptGAN, can be understood as a drop in for data augmentation to improve resilience for real world applications. Qualitative and quantitative evaluations demonstrate its efficacy in generating semantically meaningful images, as well as one shot face verification as an example application.Comment: 10 pages, 8 figures, 4 tables, CVPR 201

Semantics-based selection of everyday concepts in visual lifelogging

Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach

Maximum Likelihood Estimation with Emphasis on Aircraft Flight Data

Accurate modeling of flexible space structures is an important field that is currently under investigation. Parameter estimation, using methods such as maximum likelihood, is one of the ways that the model can be improved. The maximum likelihood estimator has been used to extract stability and control derivatives from flight data for many years. Most of the literature on aircraft estimation concentrates on new developments and applications, assuming familiarity with basic estimation concepts. Some of these basic concepts are presented. The maximum likelihood estimator and the aircraft equations of motion that the estimator uses are briefly discussed. The basic concepts of minimization and estimation are examined for a simple computed aircraft example. The cost functions that are to be minimized during estimation are defined and discussed. Graphic representations of the cost functions are given to help illustrate the minimization process. Finally, the basic concepts are generalized, and estimation from flight data is discussed. Specific examples of estimation of structural dynamics are included. Some of the major conclusions for the computed example are also developed for the analysis of flight data

The ecology of management concepts

How does the popularity of a concept depend on how it contrasts with and complements existing concepts? We argue that being similar to existing concepts, being located in a popular domain, and being combined with similar existing concepts are important for gaining attention early on but less important and even negative for sustaining popularity. To examine this question, we focus on the rise and fall of management concepts. We analyze data on the rise and fall of keywords in the Harvard Business Review between 1922 and 2010. Multiple tests confirm our hypotheses. The implication is that lessons learned from studies of popular concepts can be misleading as guides for how to make novel concepts popular

Visual representation of concepts : exploring users’ and designers’ concepts of everyday products

To address the question on how to enhance the design of user-artefact interaction at the initial stages of the design process, this study focuses on exploring the differences between designers and users in regard to their concepts of an artefact usage. It also considers that human experience determines people’s knowledge and concepts of the artefacts they interact with, and broadens or limits their concept of context of use. In this exploratory study visual representation of concepts is used to elicit information from designers and users, and to explore how these concepts are influenced by their individual experience. Observation, concurrent verbal and retrospective protocols and thematic interviews are employed to access more in depth information about users’ and designers’ concepts. The experiment was conducted with designers and users who were asked about their concepts of an everyday product. Three types of data were produced in each session: sketches, transcriptions from retrospectives verbal reports and observations. Through an iterative process, references about context, use and experience were identified in the data collected; this led to the definition of a coding system of categories that was applied for the interpretation of visuals and texts. The methodology was tested through preliminary studies. Their initial outcomes indicate that the main differences between designers’ and users’ concepts come from their knowledge domain, while main similarities are related to human experience as source that drives concept formulation. Cultural background has been found to influence concepts about product usability and its context of use. The use of visual representation of concepts with retrospective reports and interviews allowed access to insightful information on how human experience influence people’s knowledge about product usability and its context of use. It is expected that this knowledge contributes to the enhancement of the design of product usability