Description
As histopathology services continue to transition from traditional microscopy-based evaluation to fully digital acquisition, management, data transfer, and image interpretation the opportunities and possibilities for improving patient support are also met by new challenges created by the digitisation process. The high-resolution and high-dimensional images bring new data-driven discoveries of features and information that have previously been imperceptible to the human eye. The rapid expansion and accessibility of deep learning models in this field have led to early success stories and a significant amount of activity occurring in this field. This talk will cover a range of computational solutions using generative models, classification, segmentation, and explanation. But in all of these advancements in AI and adoption of computational tools, where are the people that matter? In this talk we will explore where all the stakeholders, from patient to pathologist, can be found and can be impactful in the development of novel computational techniques for patient decision making. Furthermore, we explore philosophies for evaluating AI readiness beyond evaluation metrics.Period | 16 May 2024 |
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Event title | 18th International Symposium on Medical Information and Communication Technology 2024 |
Event type | Conference |
Location | London, United KingdomShow on map |
Degree of Recognition | International |
Documents & Links
Related content
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Research output
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HPV detection in oropharyngeal squamous cell carcinoma: comparison of morphology and artificial intelligence
Research output: Contribution to journal › Meeting abstract › peer-review
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Automated Ki-67 proliferation scoring from histopathology images using Mobile and Cloud technology
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Multi-channel auto-encoders for learning domain invariant representations enabling superior classification of histopathology images
Research output: Contribution to journal › Article › peer-review
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Model performance doesn’t tell the full story – putting AI metrics under the microscope
Research output: Contribution to conference › Poster › peer-review
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Prizes
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IEEE Senior Member
Prize: Other distinction
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Queen's University Belfast: Excellence in Teaching by a Team Award
Prize: Prize (including medals and awards)
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Queen's University Belfast: Innovation in Teaching Fund - £1400
Prize: Other distinction
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Student theses
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Deep learning for processing histopathology images
Student thesis: Doctoral Thesis › Doctor of Philosophy
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Activities
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IEEE Engineering in Medicine and Biology Society (EMBS) UK and Ireland Chapter (External organisation)
Activity: Membership types › Membership of national or international committees and working groups