Switched auxiliary loss for robust training of transformer models for
  histopathological image segmentation
Mustaffa Hussain Saharsh Barve
Functional tissue Units (FTUs) are cell population neighborhoods local to a
particular organ performing its main function. The FTUs provide crucial
information to the pathologist in understanding the disease affecting a
particular organ by providing information at the cellular level. In our
research, we have developed a model to segment multi-organ FTUs across 5 organs
namely: the kidney, large intestine, lung, prostate and spleen by utilizing the
HuBMAP + HPA - Hacking the Human Body competition dataset. We propose adding
shifted auxiliary loss for training models like the transformers to overcome
the diminishing gradient problem which poses a challenge towards optimal
training of deep models. Overall, our model achieved a dice score of 0.793 on
the public dataset and 0.778 on the private dataset and shows a 1% improvement
with the use of the proposed method. The findings also bolster the use of
transformers models for dense prediction tasks in the field of medical image
analysis. The study assists in understanding the relationships between cell and
tissue organization thereby providing a useful medium to look at the impact of
cellular functions on human health.