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SUMMARY:Cambridge MedAI Seminar Series -  Jonathan Weir-McCall
DTSTART:20240521T110000Z
DTEND:20240521T120000Z
UID:TALK216988@talks.cam.ac.uk
CONTACT:Ines Machado
DESCRIPTION:The *Cancer Research UK Cambridge Centre* and the *Department 
 of Radiology at Addenbrooke's* are pleased to announce a seminar series on
  *Artificial Intelligence (AI) in Medicine*\, which aims to provide a comp
 rehensive overview of the latest developments in this rapidly evolving fie
 ld. As AI continues to revolutionize healthcare\, we believe it is essenti
 al to explore its potential and discuss the challenges and opportunities i
 t presents.\n\nThe seminar series will feature prominent experts in the fi
 eld who will share their research and insights on a range of topics\, incl
 uding AI applications in disease diagnosis\, drug discovery\, and patient 
 care. Each seminar will involve two talks\, followed by an interactive dis
 cussion with a light lunch from Aromi! We hope that this seminar series wi
 ll be a valuable platform for researchers\, practitioners and students to 
 learn about the latest trends and explore collaborations in the exciting f
 ield of AI in Medicine.\n\nThe next seminar will be held on *21 May 2024\,
  12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre)\, Un
 iversity of Cambridge* and streamed online via Zoom. A *light lunch from A
 romi will be served from 11:50*. This month will feature the following two
  talks:\n\n*Big Data and AI in Cardiac Imaging - Making a difference? - Jo
 nathan Weir-McCall\, Assistant Professor\, Department of Radiology\, Unive
 rsity of Cambridge*\n\n\nJonathan is a University Lecturer at the Universi
 ty of Cambridge and an Honorary Consultant Cardiothoracic Radiologist at t
 he Royal Papworth Hospital. His research interests lie in the use of cardi
 ovascular CT and MRI for better understanding how these can be used to imp
 rove patient treatment and outcomes in structural and coronary artery dise
 ase.  He has authored >130 peer reviewed publications\, co-authored the SC
 CT guidelines on the role of CT in the assessment for transcatheter aortic
  valve insertion\, and the BSCI/BSTI guidelines on the reporting of calcif
 ication on routine chest CT. He sits on the executive committee of the BSC
 I\, guideline committee of the SCCT\, and Diagnostic Advisory Committee of
  NICE. \n\nAbstract: AI and advanced analytics are reaching clinical pract
 ice with significant opportunities\, but also challenges in determining th
 eir real world impact and efficacy. In cardiac imaging\, advanced analytic
 s using AI and computational fluid dynamics are being routinely used in cl
 inical care. While small scale randomised control trials present promising
  insights into their potential benefits\, real world data is lacking. Leve
 raging national datasets we analyse the impact of these technologies in th
 e UK\, examining the impact of one AI-augmented CT tool on healthcare beha
 viours and patient outcomes. \n\n*Learning structures in multimodal pathol
 ogy - Konstantin Hemker\, PhD Candidate\, Computer Laboratory\, University
  of Cambridge*\n\nKonstantin is a PhD student in the Computer Lab at the U
 niversity of Cambridge focussing on multimodal representation learning for
  biomedical data modalities. In particular\, he is looking at how fusion m
 odels can provide multi-scale context in computational pathology. Before s
 tarting his PhD\, Konstantin worked as a Senior Data Scientist in the Heal
 thcare and Pharmaceuticals practice at the Boston Consulting Group\, focus
 sing on drug yield optimisation of active ingredients in antibody treatmen
 ts and radiocontrast agents. He holds Master's degrees in Computer Science
  from Imperial and Cambridge and an undergraduate degree from the London S
 chool of Economics.\n\n\n\nAbstract: Integrative modelling of multiple dat
 a structures (such as images\, graphs\, sequences\, or tabular data) in th
 e same model is a common challenge for machine learning approaches in biom
 edical domains. This challenge arises from a lack of shared semantics betw
 een modalities\, one-to-many relationships\, missing modalities\, and data
  sparsity. Meanwhile\, multi-scale context can provide important informati
 on about the tumour microenvironment in fields such as computational patho
 logy and consequently help train better predictive models. This talk will 
 cover state-of-the-art multimodal representation learning methods that can
  learn from multiple data distributions\, capture cross-modal relationship
 s\, and handle missing modalities whilst maintaining structural informatio
 n from each modality for predictive tasks in pathology.\n\n\nThis is a *hy
 brid event* so you can also join via *Zoom*: \n\nhttps://zoom.us/j/9905046
 7573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09\n\nMeeting ID: 990 5046 7573 and
  Passcode: 617729\n\n\n\nWe look forward to your participation! If you are
  interested in getting involved and presenting your work\, please email In
 es Machado at im549@cam.ac.uk\n\n
LOCATION:effrey Cheah Biomedical Centre (Main Lecture Theatre)\, Universit
 y of Cambridge
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