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SUMMARY:AI in Medicine Seminar Series - AI in Medicine Seminar Series
DTSTART:20230627T100000Z
DTEND:20230627T110000Z
UID:TALK202711@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 f
 ield who will share their research and insights on a range of topics\, inc
 luding AI applications in disease diagnosis\, drug discovery\, and patient
  care. Each seminar will also include a Q&A session to facilitate discussi
 on and exchange of ideas among participants. \n\nThe next seminar will be 
 held on the *27th of June 2023 at 11AM at the Seminar Room 12\, School of 
 Clinical Medicine*\, and will feature: \n\n*Distributional and relational 
 inductive biases for graph representation learning in biomedicine – Paul
  Scherer\, Department of Computer Science and Technology\, University of C
 ambridge* \n\nThe immense complexity in which biomolecular entities intera
 ct amongst themselves\, with one another\, and the environment to bring ab
 out life processes motivates the mass collection of biomolecular data and 
 data-driven modelling to gain insights into physiological phenomena. Grand
  initiatives and continuing efforts have been coordinated to also structur
 e our growing knowledge and understanding of biology (and beyond) within g
 raph structured data. The (re)-emerging field of representation learning o
 n graph structured data opens opportunities combine these streams of resea
 rch to leverage prior knowledge on the structure of the data and construct
  models with improved performance or interpretability. This talk will disc
 uss at a high-level how we may leverage the relational structures in biome
 dical knowledge and data to incorporate biologically relevant inductive bi
 ases into neural machine learning methods. This will be accompanied by con
 siderations to make when designing relational inductive biases over some a
 pplications I have worked on that explore different scenarios under which 
 graph structure arises in the data  \n\n*Deep learning for segmentation of
  the Venous Tumour Thrombus in MRI – Robin Haljak\, Department of Physic
 s\, University of Cambridge*\n\nAn unusual hallmark of kidney cancer is th
 e biological predisposition for vascular invasion\, with the extension of 
 the venous tumour thrombus (VTT) into the inferior vena cava occurring in 
 4-15% of cases. Automated segmentation of the VTT would be beneficial for 
 the diagnostic evaluation of kidney cancer. However\, the location\, size 
 and shape of the VTT are highly variable\, making the automatic segmentati
 on task difficult. Deep learning-based automatic segmentations of the VTT 
 were created for the first time\, using the nnU-Net segmentation framework
 . A two-stage localization-refinement-based 3D nnU-Net model is proposed t
 o significantly increase the segmentation accuracy of the VTT in kidney ca
 ncer MRI scans. The proposed model involves two main steps. In the first s
 tep\, the VTT is localised\, and an initial segmentation is created. In th
 e second step\, the segmentation is expanded and refined to more accuratel
 y segment the VTT. Training and comparative experiments were conducted on 
 the NAXIVA clinical trial data set. \n\nEach session will involve two talk
 s\, followed by an interactive discussion with coffee and pastries! We hop
 e that this seminar series will be a valuable platform for researchers\, p
 ractitioners\, and students to learn about the latest trends and explore c
 ollaborations in the exciting field of AI in Medicine. \n\nThis is a *hybr
 id event* so you can also join via Zoom: \n\nhttps://zoom.us/j/99050467573
 ?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09 \n\nMeeting ID: 990 5046 7573 and Pa
 sscode: 617729 \n\nWe look forward to your participation! \n\nIf you are i
 nterested in getting involved and presenting your work\, please email Ines
  Machado at im549@cam.ac.uk  
LOCATION:School of Clinical Medicine\, Seminar Room 12
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