Introduction
As part of the Turing's Clinical AI Interest Group, we have formed supra-interest groups in different clinical specialties to bring together clinicians and AI experts in a more focused manner - see webpage for details of all the groups.
The current supra-interest groups are:
- Anaesthetics and Intensive Care
- Medical imaging and computer vision
- AI for Women's Health
- Pathology
- Public Health
- NeuroAI
This event is the fifth meeting of the AI for Women's Health Group. This group is being lead by Bianca Schor, Kristin Collett Caolo and Annalisa Occhipinti with additional help from the organisers of the Clinical AI Interest Group.
The AI for Women's Health Group is an interdisciplinary community (e.g. clinicians, academics, data scientists, designers, health care professionals, students etc) interested in advancing women's health and how to leverage AI to do so.
The groups goals include:
- Building an interdisciplinary and diverse community of individuals working or interested to work in women's health with (or without) AI
- Bringing visibility to research on women's health and how AI can help to do so
- Facilitating collaborations and bringing more people to the field
- Offering relevant events & trainings, and sharing resources with the community (mostly online for now)
About the event
This is the fifth meeting of the AI for Women's Health Group.
In this meeting, we will have two talks from:
- Monika Golinska, PhD Eng, Medical University of Lodz & CRUK CI, University of Cambridge.
- Title of talk: Improving diagnosis of endometriosis – molecular and imaging biomarkers.
Bio: She has recently joined the Biostatistics and Translational Medicine team at the Medical University of Lodz to lead the MCSA funded project titled “Identifying angiogenic and metabolic biomarkers in endometriosis-associated ovarian cancer using photoacoustic imaging and data mining (ENDOVO).”. The experimental part of the project will be carried out as a collaboration at CRUK CI, the University of Cambridge.
She has a completed a PhD in Prof Griffiths lab at CRUK CI. Recently she has worked as a Senior Scientific Associate at Professor Bohndiek lab at Cancer Research UK Cambridge Institute. Her research focused on the role of metabolism and angiogenesis in breast and ovarian cancer. To study those phenomena, she used photoacoustic imaging and multi-omics approaches.
- Le Minh Thao Doan, PhD Student, Teeside University.
- Title of talk: An explainable multi-modal machine learning approach to predict
breast cancer outcomes - Bio: Le Minh Thao Doan is a Ph.D. student and a part-time Lecturer in Computer Science at the School of Computing, Engineering, and Digital Technologies, Teesside University, UK. Her research mainly focuses on eXplainable Artificial Intelligence (XAI) and multi-modal deep learning for breast cancer. She is working on the integration of different omics to explore the biological mechanisms underlying breast cancer progression. By leveraging complementary information from multi-omics and computational-based approaches, she aims to improve the performance and biological interpretation of deep learning models, supporting early detection methods and improving patient survival outcomes.
- Title of talk: An explainable multi-modal machine learning approach to predict
Register now
Please register for this event using this link: https://turing-uk.zoom.us/meeting/register/tJIrc-CqqDMtHN3zhZCP4GnH8Mi5_zrrt5sX
