  The encoder-decoder network is widely used to learn deep feature
representations from pixel-wise annotations in biomedical image analysis. Under
this structure, the performance profoundly relies on the effectiveness of
feature extraction achieved by the encoding network. However, few models have
considered adapting the attention of the feature extractor even in different
kinds of tasks. In this paper, we propose a novel training strategy by adapting
the attention of the feature extractor according to different tasks for
effective representation learning. Specifically, the framework, named T-Net,
consists of an encoding network supervised by task-specific attention maps and
a posterior network that takes in the learned features to predict the
corresponding results. The attention map is obtained by the transformation from
pixel-wise annotations according to the specific task, which is used as the
supervision to regularize the feature extractor to focus on different locations
of the recognition object. To show the effectiveness of our method, we evaluate
T-Net on two different tasks, i.e. , segmentation and localization. Extensive
results on three public datasets (BraTS-17, MoNuSeg and IDRiD) have indicated
the effectiveness and efficiency of our proposed supervision method, especially
over the conventional encoding-decoding network.
