CausalCellSegmenter: Causal Inference inspired Diversified Aggregation
  Convolution for Pathology Image Segmentation
Dawei Fan Yifan Gao Jiaming Yu Yanping Chen Wencheng Li Chuancong Lin Kaibin Li Changcai Yang Riqing Chen Lifang Wei
Deep learning models have shown promising performance for cell nucleus
segmentation in the field of pathology image analysis. However, training a
robust model from multiple domains remains a great challenge for cell nucleus
segmentation. Additionally, the shortcomings of background noise, highly
overlapping between cell nucleus, and blurred edges often lead to poor
performance. To address these challenges, we propose a novel framework termed
CausalCellSegmenter, which combines Causal Inference Module (CIM) with
Diversified Aggregation Convolution (DAC) techniques. The DAC module is
designed which incorporates diverse downsampling features through a simple,
parameter-free attention module (SimAM), aiming to overcome the problems of
false-positive identification and edge blurring. Furthermore, we introduce CIM
to leverage sample weighting by directly removing the spurious correlations
between features for every input sample and concentrating more on the
correlation between features and labels. Extensive experiments on the
MoNuSeg-2018 dataset achieves promising results, outperforming other
state-of-the-art methods, where the mIoU and DSC scores growing by 3.6% and
2.65%.