From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal Images

Understand

Deep learning based models, generally, require a large number of samples for appropriate training, a requirement that is difficult to satisfy in the medical field.

• This issue can usually be avoided with a proper initialization of the weights.
• On the task of medical image segmentation in general, two techniques are oftentimes employed to tackle the training of a deep network $f_T$.
• The first one consists in reusing some weights of a network $f_S$ pre-trained on a large scale database ($e.g.$ ImageNet).