Adaptive Region Selection for Active Learning in Whole Slide Image
  Semantic Segmentation
Jingna Qiu Frauke Wilm Mathias Öttl Maja Schlereth Chang Liu Tobias Heimann Marc Aubreville Katharina Breininger
The process of annotating histological gigapixel-sized whole slide images
(WSIs) at the pixel level for the purpose of training a supervised segmentation
model is time-consuming. Region-based active learning (AL) involves training
the model on a limited number of annotated image regions instead of requesting
annotations of the entire images. These annotation regions are iteratively
selected, with the goal of optimizing model performance while minimizing the
annotated area. The standard method for region selection evaluates the
informativeness of all square regions of a specified size and then selects a
specific quantity of the most informative regions. We find that the efficiency
of this method highly depends on the choice of AL step size (i.e., the
combination of region size and the number of selected regions per WSI), and a
suboptimal AL step size can result in redundant annotation requests or inflated
computation costs. This paper introduces a novel technique for selecting
annotation regions adaptively, mitigating the reliance on this AL
hyperparameter. Specifically, we dynamically determine each region by first
identifying an informative area and then detecting its optimal bounding box, as
opposed to selecting regions of a uniform predefined shape and size as in the
standard method. We evaluate our method using the task of breast cancer
metastases segmentation on the public CAMELYON16 dataset and show that it
consistently achieves higher sampling efficiency than the standard method
across various AL step sizes. With only 2.6\% of tissue area annotated, we
achieve full annotation performance and thereby substantially reduce the costs
of annotating a WSI dataset. The source code is available at
https://github.com/DeepMicroscopy/AdaptiveRegionSelection.