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  • 1.
    Jarkman, Sofia
    et al.
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Clinical pathology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lindvall, Martin
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Hedlund, Joel
    Linköping University, National Supercomputer Centre (NSC). Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Treanor, Darren
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Clinical pathology.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    van der Laak, Jeroen
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Axillary lymph nodes in breast cancer cases2019Data set
  • 2.
    Lindvall, Martin
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Sectra AB, Linköping, Sweden.
    Molin, Jesper
    Sectra AB, Linköping, Sweden.
    Designing for the Long Tail of Machine Learning2020Manuscript (preprint) (Other academic)
    Abstract [en]

    Recent technical advances has made machine learning (ML) a promising component to include in end user facing systems. However, user experience (UX) practitioners face challenges in relating ML to existing user-centered design processes and how to navigate the possibilities and constraints of this design space. Drawing on our own experience, we characterize designing within this space as navigating trade-offs between data gathering, model development and designing valuable interactions for a given model performance. We suggest that the theoretical description of how machine learning performance scales with training data can guide designers in these trade-offs as well as having implications for prototyping. We exemplify the learning curve's usage by arguing that a useful pattern is to design an initial system in a bootstrap phase that aims to exploit the training effect of data collected at increasing orders of magnitude.

  • 3.
    Lindvall, Martin
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.
    Molin, Jesper
    Sectra AB, Linköping, Sweden.
    Verification Staircase: a Design Strategy for Actionable Explanations2020In: Proceedings of the Workshop on Explainable Smart Systems for Algorithmic Transparency in Emerging Technologies co-located with 25th International Conference on Intelligent User Interfaces (IUI 2020), Cagliari, Italy, March 17, 2020 / [ed] Alison Smith-Renner and Styliani Kleanthous and Brian Lim and Tsvi Kuflik and Simone Stumpf and Jahna Otterbacher and Advait Sarkar and Casey Dugan and Avital Shulner Tal, Aachen: CEUR-WS.org , 2020, Vol. 2582Conference paper (Refereed)
    Abstract [en]

    What if the trust in the output of a predictive model could be acted upon in richer ways than a simple binary decision of accept or reject? Designing assistive AI tools for medical specialists entails supporting a complex but safety-critical decision process. It is common that decisions in this domain can be decomposed to a combination of many smaller decisions. In this paper, we present Verification Staircase – a design strategy that can be used for such scenarios. The verification staircase is when multiple interactive assistive tools are combined to allow for a nuanced amount of automation to aid the user. This can support a wide range of prediction quality scenarios, spanning from unproblematic minor mistakes to misleading major failures. By presenting the information in a hierarchical way, the user is able to learn how underlying predictions are connected to overall case predictions, and over time, calibrate their trust so that they can choose the appropriate level of automatic support.

  • 4.
    Lindvall, Martin
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB.
    Molin, Jesper
    Sectra AB.
    Löwgren, Jonas
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    From machine learning to machine teaching: the importance of UX2018In: interactions, ISSN 1072-5520, E-ISSN 1558-3449, Vol. 25, no 6, p. 52-57Article in journal (Refereed)
  • 5.
    Skoglund, Karin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Clinical pathology.
    Rose, Jeronimo
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lindvall, Martin
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB, Linköping, Sweden.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB, Linköping, Sweden.
    Treanor, Darren
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Clinical pathology. St. James University Hospital, Leeds, UK.
    Annotations, ontologies, and whole slide images: Development of an annotated ontology-driven whole slide image library of normal and abnormal human tissue2019In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 10, no 22Article in journal (Refereed)
    Abstract [en]

    Objective: Digital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, “deep learning” algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations. These large image collections are a potent source of information, and to use and share the information, standardization of the content through a consistent terminology is essential. The aim of this project was to develop a pilot dataset of exhaustive annotated WSI of normal and abnormal human tissue and link the annotations to appropriate ontological information. 

    Materials and Methods: Several biomedical ontologies and controlled vocabularies were investigated with the aim of selecting the most suitable ontology for this project. The selection criteria required an ontology that covered anatomical locations, histological subcompartments, histopathologic diagnoses, histopathologic terms, and generic terms such as normal, abnormal, and artifact. WSIs of normal and abnormal tissue from 50 colon resections and 69 skin excisions, diagnosed 2015-2016 at the Department of Clinical Pathology in Linköping, were randomly collected. These images were manually and exhaustively annotated at the level of major subcompartments, including normal or abnormal findings and artifacts. 

    Results: Systemized nomenclature of medicine clinical terms (SNOMED CT) was chosen, and the annotations were linked to its codes and terms. Two hundred WSI were collected and annotated, resulting in 17,497 annotations, covering a total area of 302.19 cm2, equivalent to 107,7 gigapixels. Ninety-five unique SNOMED CT codes were used. The time taken to annotate a WSI varied from 45 s to over 360 min, a total time of approximately 360 h. 

    Conclusion: This work resulted in a dataset of 200 exhaustive annotated WSIs of normal and abnormal tissue from the colon and skin, and it has informed plans to build a comprehensive library of annotated WSIs. SNOMED CT was found to be the best ontology for annotation labeling. This project also demonstrates the need for future development of annotation tools in order to make the annotation process more efficient.

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  • 6.
    Tran Luciani, Danwei
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Lindvall, Martin
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB, Sweden.
    Löwgren, Jonas
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Machine learning as a design material: a curated collection of exemplars for visual interaction2018In: DS 91: Proceedings of NordDesign 2018, Linköping, Sweden, 14th - 17th August 2018 / [ed] Philip Ekströmer, Simon Schütte and Johan Ölvander, 2018, p. 1-10Conference paper (Refereed)
    Abstract [en]

    Although machine learning is not a new phenomenon, it has truly entered the spotlight in recent years. With growing expectations, we see a shift in focus from performance tuning to awareness of meaningful interaction and purpose. Interaction design and UX research is currently in a position to provide important and necessary knowledge contributions to the development of machine learning systems. Machine learning can be viewed as a design material that is arguably more unpredictable, emergent, and “alive” than traditional ones. These characteristics suggest practice-based work along the lines of research-through-design as a promising approach for machine learning system development research. Design researchers using a research-through-design approach agree that a created artefact carries knowledge, but there is no consensus on how such knowledge is best articulated and transferred within academic discourse. Knowledge contributions need to be abstracted from the particular to a higher level. We suggest curated collections, a variation of annotated portfolios, as a way to abstract and communicate intermediate-level knowledge that is suitable and useful for the research-through-design community. A curated collection presents thoughtfully selected and inter-related exemplars, articulating their salient traits. The insights collected in a curated collection can be used to inform future design in related design situations. This paper provides a curated collection addressing the fine-grained details of interaction with machine learning systems. The examples are drawn from highly visual interaction, predominantly in the domain of digital pathology. The collection of interaction examples is used to elicit a set of salient traits, including the preservation of visual context, rapid real-time refinement, leaving traces, and applying judicious automation. Finally, we show how this curated collection could inform the design of a future system in a different domain. The insights are applied to a case of interaction design to support air traffic controllers in their collaboration with future agentive systems

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