Intézeti szeminárium

Developing clinical predictive models by processing medical radiology
images is often challenging due to high variability of data, noise and
data scarcity. Using pre-trained feature extractors in deep learning
configurations to initialise weights is often beneficial to the model
optimisation process, leading to faster convergence and more accurate
models. Although one can also benefit from transferring knowledge from
other domains, using specialised domains is usually the better choice
because it makes the shift in the embedding distribution smaller. This
requires an annotated medical radiology image dataset that is diverse,
large and challenging enough to produce generally useful embeddings
spanning imaging modalities and anatomical regions.

In this talk, I will discuss some of the challenges associated with
processing medical radiology images and supporting EHR data from PACS
of clinical centres. I will also present some of the work we have done
in developing an automated annotation system for the PACS/EHR archive
of CHC Rijeka, with the aim of learning generally useful embeddings
for transfer learning in medical radiology image processing.