Modular Deep Active Learning Framework for Image Annotation: A Technical
  Report for the Ophthalmo-AI Project
Md Abdul Kadir Hasan Md Tusfiqur Alam Pascale Maul Hans-Jürgen Profitlich Moritz Wolf Daniel Sonntag
Image annotation is one of the most essential tasks for guaranteeing proper
treatment for patients and tracking progress over the course of therapy in the
field of medical imaging and disease diagnosis. However, manually annotating a
lot of 2D and 3D imaging data can be extremely tedious. Deep Learning (DL)
based segmentation algorithms have completely transformed this process and made
it possible to automate image segmentation. By accurately segmenting medical
images, these algorithms can greatly minimize the time and effort necessary for
manual annotation. Additionally, by incorporating Active Learning (AL) methods,
these segmentation algorithms can perform far more effectively with a smaller
amount of ground truth data. We introduce MedDeepCyleAL, an end-to-end
framework implementing the complete AL cycle. It provides researchers with the
flexibility to choose the type of deep learning model they wish to employ and
includes an annotation tool that supports the classification and segmentation
of medical images. The user-friendly interface allows for easy alteration of
the AL and DL model settings through a configuration file, requiring no prior
programming experience. While MedDeepCyleAL can be applied to any kind of image
data, we have specifically applied it to ophthalmology data in this project.