Imaging for clinical interpretation or intervention is a key element of medicine, and there is currently a rapid growth in the number of medical imaging studies. The main challenge for clinicians is to interpret the complexity and dynamic changes of these images. Currently most such interpretations are performed by human experts, but this can be time-consuming, expensive, and error prone due to visual fatigue. Medical image computing has emerged as an interdisciplinary field to develop robust and accurate computational methods to extract clinically relevant information.
Recent advances in deep learning show that computers can extract more information from images, with an increase in reliability and accuracy. Moreover, deep learning can be used to identify and extract novel features that would otherwise not be easily accessible to human viewers.
The major challenge is now to develop and adapt these techniques to enhance:
- Computer-aided detection and diagnosis
- Image analysis with deep learning to enhance interventions
- Integration of imaging and clinical data
- End-to-end learning for prognosis and treatment selection
We are looking into running an afternoon demo session so timings may vary in the afternoon.
As with all CW events, academic institutions, students, not for profits and professionals working in the sector but unable to afford the ticket price can apply for free tickets. Please contact Rachel.kerr@cambridgewireless.co.uk to explore this opportunity.
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