Switched auxiliary loss for robust training of transformer models for histopathological image segmentation

TL;DR

This paper introduces a switched auxiliary loss method for training transformer models to improve the segmentation of functional tissue units across multiple organs in histopathological images, demonstrating robustness and effectiveness.

Contribution

The study proposes a novel switched auxiliary loss technique for transformer training, enhancing segmentation performance in medical imaging tasks.

Findings

Achieved a dice score of 0.793 on the public dataset.

Demonstrated robustness of the training methodology.

Supported the use of transformers for dense prediction in medical imaging.

Abstract

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.