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.

翻译：功能组织单元（FTUs）是执行特定器官主要功能的细胞群体邻域。通过提供细胞层面的信息，FTUs为病理学家理解影响特定器官的疾病提供了关键信息。在本研究中，我们利用“HuBMAP + HPA - 破解人体”竞赛数据集，开发了一个模型用于分割跨越5个器官（即肾脏、大肠、肺、前列腺和脾脏）的多器官FTUs。我们提出在训练Transformer等模型时添加切换辅助损失，以克服梯度消失问题，该问题对深度模型的最优训练构成挑战。总体而言，我们的模型在公开数据集上获得了0.793的Dice分数，在私有数据集上获得了0.778的Dice分数。结果支持了所提训练方法的鲁棒性。这些发现也支持了在医学图像分析领域将Transformer模型用于密集预测任务。该研究有助于理解细胞与组织结构之间的关系，从而为观察细胞功能对人类健康的影响提供了一个有用的媒介。