提出轻量级ViT架构，解决肝病评分多任务干扰问题
Parameter-Efficient Subspace Decoupling ViT for Mitigating Multi-Task Negative Transfer in Histological Scoring
- 用正交约束的Adapter解耦三个病理特征子空间
- 在小鼠数据集上实现比单任务模型更低计算开销
- 适合需要高效多任务医学图像分析的研究者
组织学评分对非酒精性脂肪肝病（NAFLD）诊断至关重要，但其自动化因标注成本高及多任务学习中强相关指标（如脂肪变性、气球样变、炎症）间负迁移而困难。本文提出一种子空间解耦的多任务视觉变换器（ViT），通过轻量级任务特定Adapter与基于正交性的约束，为脂肪变性、气球样变和炎症构建独立特征子空间，有效降低任务干扰同时保留共享表示。我们进一步构建了经专家标注的多任务小鼠NAFLD组织学数据集，涵盖所有NAS成分。实验表明，该方法在保持多任务稳定性与泛化能力的同时，显著降低计算开销，优于训练独立单任务模型。代码与数据集将在论文接受后公开，以支持可复现性。
原文摘要 · Abstract (English)
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.
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