  Medical image segmentation is a relevant problem, with deep learning being an
exponent. However, the necessity of a high volume of fully annotated images for
training massive models can be a problem, especially for applications whose
images present a great diversity, such as brain tumors, which can occur in
different sizes and shapes. In contrast, a recent methodology, Feature Learning
from Image Markers (FLIM), has involved an expert in the learning loop,
producing small networks that require few images to train the convolutional
layers. In this work, We employ an interactive method for image selection and
training based on FLIM, exploring the user's knowledge. The results
demonstrated that with our methodology, we could choose a small set of images
to train the encoder of a U-shaped network, obtaining performance equal to
manual selection and even surpassing the same U-shaped network trained with
backpropagation and all training images.
