用注意力MIL模型自动数皮脂细胞脂滴，效果不如简单聚合方法稳定。
Evaluating Multiple Instance Learning Strategies for Automated Sebocyte Droplet Counting
- 用注意力机制的多实例学习框架分析皮脂细胞图像
- 基线模型平均误差5.6，注意力模型平均误差10.7
- 简单聚合法更可靠，适合初学者快速上手
皮脂细胞是分泌脂质的细胞，其分化特征为细胞内脂滴积累，因此脂滴计数是皮脂细胞生物学的关键指标。人工计数耗时且主观，推动自动化方案发展。本文提出一种基于注意力的多实例学习（MIL）框架用于皮脂细胞图像分析。使用尼罗红染色的皮脂细胞图像，按脂滴数量标注为14类，并通过数据增强扩展至约5万张细胞图像。对比两种模型：基于聚合补丁级计数的多层感知机（MLP）基线模型，以及利用ResNet-50特征并引入实例加权的注意力型MIL模型。五折交叉验证结果显示，基线MLP表现更稳定（平均绝对误差MAE = 5.6），而注意力型MIL模型表现不一致（平均MAE = 10.7），但在特定折次中表现更优。结果表明，简单的袋级别聚合可作为滑片级脂滴计数的稳健基线，而注意力型MIL需任务对齐的池化与正则化才能充分发挥潜力。
原文摘要 · Abstract (English)
Sebocytes are lipid-secreting cells whose differentiation is marked by the accumulation of intracellular lipid droplets, making their quantification a key readout in sebocyte biology. Manual counting is labor-intensive and subjective, motivating automated solutions. Here, we introduce a simple attention-based multiple instance learning (MIL) framework for sebocyte image analysis. Nile Red-stained sebocyte images were annotated into 14 classes according to droplet counts, expanded via data augmentation to about 50,000 cells. Two models were benchmarked: a baseline multi-layer perceptron (MLP) trained on aggregated patch-level counts, and an attention-based MIL model leveraging ResNet-50 features with instance weighting. Experiments using five-fold cross-validation showed that the baseline MLP achieved more stable performance (mean MAE = 5.6) compared with the attention-based MIL, which was less consistent (mean MAE = 10.7) but occasionally superior in specific folds. These findings indicate that simple bag-level aggregation provides a robust baseline for slide-level droplet counting, while attention-based MIL requires task-aligned pooling and regularization to fully realize its potential in sebocyte image analysis.
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