Histological scoring is essential for diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD), yet its automation remains challenging due to the high annotation cost and negative transfer among the strongly correlated NAFLD Activity Score (NAS) indicators in multi-task learning. To address this issue, we propose a subspace-decoupled multi-task Vision Transformer (ViT) that integrates lightweight task-specific Adapters with orthogonality-based constraints. This design constructs independent feature subspaces for steatosis, ballooning, and inflammation, effectively reducing task interference while retaining shared representations. We further construct a curated multi-task mouse NAFLD histology dataset with expert annotations for all NAS components. Experimental results demonstrate that the proposed method improves multi-task stability and generalization with substantially reduced computational cost compared to training separate single-task models. The code and the curated dataset have been prepared and will be made publicly available upon acceptance to support reproducibility.

翻译：组织学评分对非酒精性脂肪性肝病（NAFLD）的诊断至关重要，但由于标注成本高昂，且多任务学习中高度相关的NAFLD活动度评分（NAS）指标之间存在负迁移，其自动化仍面临挑战。为解决此问题，我们提出一种子空间解耦的多任务视觉Transformer（ViT），该模型集成了轻量级任务特定适配器与基于正交性的约束。该设计为脂肪变性、气球样变和炎症构建了独立特征子空间，在保留共享表征的同时有效减少任务干扰。我们进一步构建了一个经过专家标注的、包含所有NAS组分的多任务小鼠NAFLD组织学数据集。实验结果表明，与训练独立单任务模型相比，所提方法在显著降低计算成本的同时，提升了多任务稳定性与泛化能力。代码及所构建数据集已准备就绪，将在论文被接收后公开以支持可复现性。