Switched auxiliary loss for robust training of transformer models for histopathological image segmentation
This work addresses medical image segmentation for pathologists analyzing cellular-level disease information, though it appears incremental with a specific training improvement.
The researchers tackled the problem of segmenting Functional Tissue Units (FTUs) across five organs using a transformer model, achieving dice scores of 0.793 on public and 0.778 on private datasets. They proposed a switched auxiliary loss to address diminishing gradients during training.
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 switched 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.The results supports the robustness of the proposed training methodology.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.