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BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

In this work, we describe the field research, design, and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of Single-Modality vs Multi-Modality screening of breast cancer diagnosis with 31 clinicians and 566 images; and 3) The visualization of the two main types of breast lesions in the following image modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging (MRI).

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BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

  1. 1. AVI 2020 International Conference on Advanced Visual Interfaces
  2. 2. BreastScreening On the Use of Multi-Modality in Medical Imaging Diagnosis
  3. 3. Authors Francisco M. Calisto ISR-Lisboa Instituto Superior Técnico Universidade de Lisboa Nuno Nunes ITI Instituto Superior Técnico Universidade de Lisboa Jacinto C. Nascimento ISR-Lisboa Instituto Superior Técnico Universidade de Lisboa
  4. 4. Motivation Why Cancer? Why Medical Imaging? 1.
  5. 5. 9.6 million deaths in 2018 5 Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A. and Jemal, A., 2018. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), pp.394-424.
  6. 6. 30% - 50%of cancers can currently be prevented by avoiding risk factors and implementing existing evidence-based prevention strategies. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A. and Jemal, A., 2018. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), pp.394-424.
  7. 7. Early Detection Cancer mortality can be reduced if cases are detected and treated early.
  8. 8. Early Diagnosis » Care Components » Awareness » Accessing » Clinical Components » Evaluation » Diagnosis » Staging » Treatment Access There are two components of Early Detection Screening Screening aims to identify individuals with abnormalities suggestive of a specific cancer or pre-cancer.
  9. 9. HCI in Healthcare Diagnostic systems have also been studied under the HCI field. 2.
  10. 10. HCI in Healthcare » Supporting the image search UX through novel UIs [1, 2] » Medical imaging technologies to support radiologists [3] » Systems that assist radiologists in image interpretation [4, 5] 1 Koutsabasis, P. and Domouzis, C.K., 2016, June. Mid-air browsing and selection in image collections. In Proceedings of the International Working Conference on Advanced Visual Interfaces (pp. 21-27). 2 Lee, B., Srinivasan, A., Stasko, J., Tory, M. and Setlur, V., 2018, May. Multimodal interaction for data visualization. In Proceedings of the 2018 International Conference on Advanced Visual Interfaces (pp. 1-3). 3 Woźniak, P., Romanowski, A., Yantaç, A.E. and Fjeld, M., 2014, October. Notes from the front lines: lessons learnt from designing for improving medical imaging data sharing. In Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational (pp. 381-390). 4 Cai, C.J., Winter, S., Steiner, D., Wilcox, L. and Terry, M., 2019. " Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making. Proceedings of the ACM on Human-computer Interaction, 3 (CSCW), pp.1-24. 5 Oram, L., MacLean, K., Kruchten, P. and Forster, B., 2014, June. Crafting diversity in radiology image stack scrolling: control and annotations. In Proceedings of the 2014 conference on Designing interactive systems (pp. 567-576).
  11. 11. Medical Imaging How to improve and support medical imaging? 3.
  12. 12. Healthcare Systems Machines that can learn with new data from clinicians’ experience.
  13. 13. Healthcare Systems But first we need to create a solution to generate information to the algorithms.
  14. 14. Workflow Medical Images Image Visualization Medical Annotations
  15. 15. Multimodality Annotating System + Magnetic Resonance Imaging (MRI) UltraSound (US) MammoGraphy (MG) +
  16. 16. Medical Annotations
  17. 17. Medical Annotations Masses
  18. 18. Medical Annotations Microcalcifications
  19. 19. Multimodality Annotating System > Magnetic Resonance Imaging (MRI) UltraSound (US) MammoGraphy (MG) > Lesions
  20. 20. Multimodality Annotating System Interaction A system for a multimodal interaction with MG, US and MRI medical images on a multi- screen and multi- environment. Visualization The indistinct visualization of cluttered breast lesions. Big Data A platform for generating and managing big data on medical images.
  21. 21. Methods Human-centered study to understand clinician’s needs. 4.
  22. 22. 31 clinicians contributed to our study
  23. 23. 6 institutions of healthcare in Portugal.
  24. 24. Six Institutions » 8 clinicians Hospital Fernando Fonseca » 12 clinicians IPO-Lisboa » 1 clinician Hospital de Santa Maria » 8 clinicians IPO-Coimbra » 1 clinician Madeira Medical Center » 1 clinician SAMS
  25. 25. > 16 hours of observationsand interviews
  26. 26. 566 images acquiredfrom Hospital Fernando Fonseca
  27. 27. Design Goals The technical design challenges lead to a set of design issues. 5.
  28. 28. Design Issues » Medical imaging structure trade-offs » Radiology room temporal awareness [6] » Image segmentation support » Radiologists system trust 6 Nascimento, J.C. and Carneiro, G., 2019. One shot segmentation: unifying rigid detection and non-rigid segmentation using elastic regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence.
  29. 29. Five Design Goals » DIM Design around and for Medical Imaging » TAS Temporal Awareness Support » ISS Image Segmentation Support » SMS Several Modalities Support » GTO Growing Trust Overview
  30. 30. Design around and for Medical Imaging Taking into account the heterogeneous nature of medical imaging to leverage its contextual richness.
  31. 31. Temporal Awareness Support Observing how the radiology workflow events, treatments, and problems progressed over time.
  32. 32. Image Segmentation Support The overview of image details allowing a more accurate diagnostic.
  33. 33. Several Modalities Support Enable the view and the process of diagnostic imaging studies.
  34. 34. Growing Trust Overview Allowing an efficient triangulation via visualizations, image processing between medical images and available features.
  35. 35. BreastScreening A medical imaging visualization proof-of-concept to be evaluated in a realistic clinical scenario. 6.
  36. 36. User Interface
  37. 37. User Interface
  38. 38. User Interface
  39. 39. Procedures Evaluation of BreastScreening in real-world conditions. 7.
  40. 40. Cond. C1 Single-Modality Two Conditions Cond. C2 Multi-Modality 40
  41. 41. Three Patients » P1 Low (BI-RADS < 2) » P2 High (BI-RADS > 3) » P3 Medium (1 > BI-RADS > 4)
  42. 42. BREAST SEVERITY BI-RADS Meaning 0 Needs more information (more exams or waiting for more exams) 1 Negative 2 Benign 3 Probably Benign 4 Suspicious 5 Highly suggestive of malignancy 6 Known biopsy-proven malignancy
  43. 43. Quantitative Analysis Four relations emerged from our analysis. 8.
  44. 44. Quantitative Analysis » SUS Scores vs SUS Questions » Intern » Junior » Middle » Senior » NASA-TLX » Single-Modality vs Multi-Modality » Time vs Number of Clicks » P1 » P2 » P3 » BI-RADS Classification
  45. 45. SUS Scores vs SUS Questions Participants adopting the Multi-Modality condition obtained higher SUS scores than those using the Single-Modality condition.
  46. 46. Workload In general, the workload improved for Mental Demand, Physical Demand, Temporal Demand, Performance, Effort and Frustration while using the Multi- Modality setup.
  47. 47. Time vs Number of Clicks The time per image was reduced. The number of clicks was also improved. Which are directly related to the number of annotations on the lesion.
  48. 48. BI-RADS Classification MediumHighLow
  49. 49. Qualitative Analysis Clinicians were invited to give some feedback about the UI during the open interviews. 9.
  50. 50. “ The system will be a great asset for us.
  51. 51. “ I would like to frequently use your system on my daily practice.
  52. 52. Conclusion The conclusions of our work. 10.
  53. 53. Contributions » Identifying the main clinical workflow issues, the interaction cognitive load challenges and the opportunities; » Establishing a set of design goals for medical imaging design; » The design, reflections and in-situ evaluation of BreastScreening supporting the clinical translation; » The impact evidence of Multi-Modality in diagnosing and severity classification of breast lesions;
  54. 54. Contributions The system can lead to more efficient and accurate clinical diagnosis.
  55. 55. Information Francisco Maria Calisto E-Mail: francisco.calisto@tecnico.ulisboa.pt Academic Webpage: web.tecnico.ulisboa.pt/francisco.calisto Lab Webpage: welcome.isr.tecnico.ulisboa.pt
  56. 56. Thank you!
  57. 57. “The paradigm shift of the ImageNet thinking is that while a lot of people are paying attention to models, let's pay attention to data. Data will redefine how we think about models. - Li Fei-Fei
  58. 58. Q&A

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In this work, we describe the field research, design, and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of Single-Modality vs Multi-Modality screening of breast cancer diagnosis with 31 clinicians and 566 images; and 3) The visualization of the two main types of breast lesions in the following image modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging (MRI).

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