Instructive Feature Enhancement for Dichotomous Medical Image
  Segmentation
Lian Liu Han Zhou Jiongquan Chen Sijing Liu Wenlong Shi Dong Ni Deng-Ping Fan Xin Yang
Deep neural networks have been widely applied in dichotomous medical image
segmentation (DMIS) of many anatomical structures in several modalities,
achieving promising performance. However, existing networks tend to struggle
with task-specific, heavy and complex designs to improve accuracy. They made
little instructions to which feature channels would be more beneficial for
segmentation, and that may be why the performance and universality of these
segmentation models are hindered. In this study, we propose an instructive
feature enhancement approach, namely IFE, to adaptively select feature channels
with rich texture cues and strong discriminability to enhance raw features
based on local curvature or global information entropy criteria. Being
plug-and-play and applicable for diverse DMIS tasks, IFE encourages the model
to focus on texture-rich features which are especially important for the
ambiguous and challenging boundary identification, simultaneously achieving
simplicity, universality, and certain interpretability. To evaluate the
proposed IFE, we constructed the first large-scale DMIS dataset Cosmos55k,
which contains 55,023 images from 7 modalities and 26 anatomical structures.
Extensive experiments show that IFE can improve the performance of classic
segmentation networks across different anatomies and modalities with only
slight modifications. Code is available at https://github.com/yezi-66/IFE