  Weakly supervised methods, such as class activation maps (CAM) based, have
been applied to achieve bleeding segmentation with low annotation efforts in
Wireless Capsule Endoscopy (WCE) images. However, the CAM labels tend to be
extremely noisy, and there is an irreparable gap between CAM labels and ground
truths for medical images. This paper proposes a new Discrepancy-basEd Active
Learning (DEAL) approach to bridge the gap between CAMs and ground truths with
a few annotations. Specifically, to liberate labor, we design a novel
discrepancy decoder model and a CAMPUS (CAM, Pseudo-label and groUnd-truth
Selection) criterion to replace the noisy CAMs with accurate model predictions
and a few human labels. The discrepancy decoder model is trained with a unique
scheme to generate standard, coarse and fine predictions. And the CAMPUS
criterion is proposed to predict the gaps between CAMs and ground truths based
on model divergence and CAM divergence. We evaluate our method on the WCE
dataset and results show that our method outperforms the state-of-the-art
active learning methods and reaches comparable performance to those trained
with full annotated datasets with only 10% of the training data labeled.
