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Explaining Semantic Search Results of Medical Images in MEDICO

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    Explaining Semantic Search Results of Medical Images in MEDICO - Presentation Transcript

    1. Explaining Semantic Search Results of Medical Images in MEDICO Björn Forcher, Manuel Möller, Michael Sintek, and Thomas Roth-Berghofer Mittwoch, 15. Juli 2009
    2. Reality check Mittwoch, 15. Juli 2009
    3. „Trust me. I know what I am doing!“ Mittwoch, 15. Juli 2009
    4. „Trust me. I know what I am doing!“ Mittwoch, 15. Juli 2009
    5. Goal of Medico Project Development of • intelligent • robust and • scalable semantic search engine for medical images Mittwoch, 15. Juli 2009
    6. Goal of Medico Project Development of • intelligent • robust and • scalable semantic search engine for medical images Mittwoch, 15. Juli 2009
    7. Goal of Medico Project Development of • intelligent • robust and • scalable semantic search engine for medical images Mittwoch, 15. Juli 2009
    8. RadSem • Tool to support medical doctors (esp. radiologists) in annotating and searching for medical images (and text) • Part of the MEDICO project (funded by BMWi in the research programme THESEUS) • Developed together with medical experts (who have to use the tool to annotate real images) 5 Mittwoch, 15. Juli 2009
    9. Intended Users of RadSem • Medical doctors • Medical IT professionals • Patients and citizens • Policy makers Mittwoch, 15. Juli 2009
    10. MEDICO System Architecture 7 Mittwoch, 15. Juli 2009
    11. MEDICO System Architecture 7 Mittwoch, 15. Juli 2009
    12. MEDICO System Architecture 7 Mittwoch, 15. Juli 2009
    13. MEDICO Ontology Hierarchy 8 Mittwoch, 15. Juli 2009
    14. MEDICO Ontology Hierarchy 8 Mittwoch, 15. Juli 2009
    15. Foundational Model of Anatomy FMA • developed and maintained by Structural Informatics Group at University of Washington • contains more than 70.000 anatomical entities (classes) • more than 1.5 million relations between the entities • most comprehensive human ontology 9 Mittwoch, 15. Juli 2009
    16. ICD-10 in OWL • Problem: No disease terminology available in OWL • Established standard: International Classification of Diseases (WHO), but only available in semi- structured formats • Approach: Crawler for online version of ICD-10 generates light-weight OWL ontology 10 Mittwoch, 15. Juli 2009
    17. Example annotation • FMA • ICD 10 Mittwoch, 15. Juli 2009
    18. Example annotation Region of Interest • FMA • ICD 10 Mittwoch, 15. Juli 2009
    19. Example annotation Region of Interest • FMA • ICD 10 Mittwoch, 15. Juli 2009
    20. Example annotation Region of Interest • FMA • ICD 10 Mittwoch, 15. Juli 2009
    21. Example annotation Region of Interest • FMA • ICD 10 Mittwoch, 15. Juli 2009
    22. Explainer User Interface Originator Basic explanation scenario Mittwoch, 15. Juli 2009
    23. Explainer User Interface Originator Problem solving Basic explanation scenario knowledge Mittwoch, 15. Juli 2009
    24. Explanation knowledge Explainer User Interface Originator Problem solving Basic explanation scenario knowledge Mittwoch, 15. Juli 2009
    25. Explainer User Interface Originator Basic explanation scenario Mittwoch, 15. Juli 2009
    26. Explainer User Interface Originator Semantic Search Basic explanation scenario Mittwoch, 15. Juli 2009
    27. • Query expansion with ontology concepts • Count path Explainer length from search to User Interface found concept Originator Semantic Search Basic explanation scenario Mittwoch, 15. Juli 2009
    28. Motivations for explanations in RadSem • Test whether the Search Engine works correctly • Test whether the ontologies are correctly modelled • Learn about the medical domain • Justify results in order to increase trust Mittwoch, 15. Juli 2009
    29. Motivations for explanations in RadSem • Test whether the Search Engine works correctly Medical IT • Test whether the ontologies are professionals correctly modelled • Learn about the medical domain • Justify results in order to increase trust Mittwoch, 15. Juli 2009
    30. Motivations for explanations in RadSem • Test whether the Search Engine works correctly Medical IT • Test whether the ontologies are professionals correctly modelled • Learn about the medical domain Patients and • Justify results in order to increase citizens trust Mittwoch, 15. Juli 2009
    31. Motivations for explanations in RadSem • Help users to improve their search • Activate passive knowledge • Users learn how to use the engine concerning ontologies Mittwoch, 15. Juli 2009
    32. Motivations for explanations in RadSem • Help users to improve their search Medical • Activate passive knowledge doctors • Users learn how to use the engine concerning ontologies Mittwoch, 15. Juli 2009
    33. Motivations for explanations in RadSem • Help users to improve their search Medical • Activate passive knowledge doctors • Users learn how to use the engine Patients and concerning ontologies citizens Mittwoch, 15. Juli 2009
    34. What are explanations? Mittwoch, 15. Juli 2009
    35. What are explanations? Explanations are answers to questions. Mittwoch, 15. Juli 2009
    36. When are questions being asked? Mittwoch, 15. Juli 2009
    37. When are questions being asked? Whenever expectations are not met. Mittwoch, 15. Juli 2009
    38. Explanation goals • Transparency How did the system reach an answer? • Justification Why is the answer a good answer? • Relevance Why is the question relevant? • Conceptualisation What is the meaning of a concept? • Learning Teach the user about the given domain. Sørmo, F., Cassens, J., Aamodt, A.: Explanation in Case-Based Reasoning – Perspectives and Goals, 2005. Mittwoch, 15. Juli 2009
    39. When are explanations good explanations? • Short and easy to overlook • Innovative • Relevant • Convincing • Different perspectives and follow-up questions • Natural W. R. Swartout and J. D. Moore. Explanation in second generation expert systems. In J. David, J. Krivine, and R. Simmons, editors, Second Generation Expert Systems, pages 543–585, Berlin, 1993. Springer Verlag. Mittwoch, 15. Juli 2009
    40. When are explanations good explanations? • Short and easy to overlook • Innovative • Relevant • Convincing • Different perspectives and follow-up questions • Natural W. R. Swartout and J. D. Moore. Explanation in second generation expert systems. In J. David, J. Krivine, and R. Simmons, editors, Second Generation Expert Systems, pages 543–585, Berlin, 1993. Springer Verlag. Mittwoch, 15. Juli 2009
    41. Kinds of explanations • Action explanations and justifications: „How do search concepts relate to found concepts?“ • Concept explanations Mittwoch, 15. Juli 2009
    42. Action explanations • Action explanations explain the activities of the respective system (originator). Action explanations: “Why was this seat post selected?” – “For the given price, only one other seat post was available. But this was too short. • In RadSem: Reconstructive explanations based on search and found concepts. Mittwoch, 15. Juli 2009
    43. Why-explanations • Why-explanations provide causes or justifications for facts or events. • Examples: • Justification: “Why does the universe expand?” – “Because we can observe a red shift of the light emitted by other galaxies.” • Cause: “Because the whole matter was concentrated at one point of the universe and because the whole matter moves away from each other Mittwoch, 15. Juli 2009
    44. Concept Explanations • The goal of concept explanations is to build links between unknown and known concepts. • Variations: • Definition: “What is a bicycle?” – “A bicycle is a land vehicle with two wheels in line. Bicycles are a form of human powered vehicle.” • Functional mapping: “What is a bicycle?” – “A bicycle serves as a means of transport.” • Prototypical usage of individual things or actions: “What is a bicycle?” – “The thing, that man over there just crashed with.” • … Mittwoch, 15. Juli 2009
    45. Explainer User Interface Originator Semantic Search Basic explanation scenario Mittwoch, 15. Juli 2009
    46. • Dijkstra algorithm estimates semantic search Explainer User Interface Originator Semantic Search Basic explanation scenario Mittwoch, 15. Juli 2009
    47. Example search Mittwoch, 15. Juli 2009
    48. Exploration interface Mittwoch, 15. Juli 2009
    49. Exploration interface Mittwoch, 15. Juli 2009
    50. „Bridge concepts“ Mittwoch, 15. Juli 2009
    51. „Bridge concepts“ Mittwoch, 15. Juli 2009
    52. FMA problem • Same concept, different labels Mittwoch, 15. Juli 2009
    53. FMA problem • Same concept, different labels Mittwoch, 15. Juli 2009
    54. Label problems of FMA Mittwoch, 15. Juli 2009
    55. User experiment wrt explanations in RadSem • Test whether the Search Engine works correctly Medical IT • Test whether the ontologies are professionals correctly modelled • Learn about the medical domain Patients and • Justify results in order to increase citizens trust Mittwoch, 15. Juli 2009
    56. User experiment wrt explanations in RadSem • Test whether the Search Engine works correctly Medical IT • Test whether the ontologies are professionals correctly modelled • Learn about the medical domain Patients and • Justify results in order to increase citizens trust → Results supported our motivations for providing explanations. Mittwoch, 15. Juli 2009
    57. Future Work • Selection of proper labels wrt different user groups • Search for alternative paths • Exploration of paths • Tailoring of paths • Dictionary for lexical concepts • Links to Wikipedia Mittwoch, 15. Juli 2009
    58. Take home messages Mittwoch, 15. Juli 2009
    59. Take home messages • RadSem is a complex annotation and search tool. Mittwoch, 15. Juli 2009
    60. Take home messages • RadSem is a complex annotation and search tool. • Goals and kinds of explanations are a useful tool in designing a software system in an explanation-aware manner. Mittwoch, 15. Juli 2009
    61. Take home messages • RadSem is a complex annotation and search tool. • Goals and kinds of explanations are a useful tool in designing a software system in an explanation-aware manner. Explainer • Basic explanation scenario helps User identify communication partners Originator Mittwoch, 15. Juli 2009
    62. Take home messages • RadSem is a complex annotation and search tool. • Goals and kinds of explanations are a useful tool in designing a software system in an explanation-aware manner. Explainer • Basic explanation scenario helps User identify communication partners Originator • Exploration interface with concept explanations support domain understanding. Mittwoch, 15. Juli 2009
    63. Take home messages • RadSem is a complex annotation and search tool. • Goals and kinds of explanations are a useful tool in designing a software system in an explanation-aware manner. Explainer • Basic explanation scenario helps User identify communication partners Originator • Exploration interface with concept explanations support domain understanding. • Justification interface provides action explanations, which counteract encapsulation and information hiding. Mittwoch, 15. Juli 2009
    64. Thank you! Explaining Semantic Search Results of Medical Images in MEDICO Thomas Roth-Berghofer Senior researcher, trb@dfki.de German Research Centre for Artificial Intelligence DFKI GmbH Mittwoch, 15. Juli 2009
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