Mueller matrix imaging provides rich, physically meaningful contrast for biomedical tissue analysis, but supervised learning is hindered by scarce dense annotations and strong domain shifts across specimens and acquisition settings. We introduce MuellerPT, a physics guided pre-training approach that learns transferable dense representations by predicting Lu-Chipman decomposition maps from per-pixel 4x4 Mueller matrices. To scale pre-training, we collected a new large Multispectral Animal Polarimetric Organ dataset (MAP-Org). The pre-trained encoder is adapted with a segmentation head for grey vs. white matter segmentation in lamb brain. A classification head is used for colorectal cancer vs. non-cancer classification. Both segmentation and classification are evaluated across few-shot learning scenarios. In segmentation, MuellerPT improves label efficiency and cross specimen transfer compared to models without pre-training, achieving an absolute DICE gain of over 20% compared to the baseline trained from scratch when using 5% of the training data. In classification, MuellerPT also enhances label efficiency, improving overall accuracy by 8% compared to the baseline when using 1% of the training data. We demonstrate MuellerPT's robustness to domain shift with a qualitative evaluation of its predicted Lu-Chipman maps on an ex vivo human oesophagus sample. These results suggest that predicting Lu-Chipman decomposition is an effective and practical pretext task for robust biomedical inference from Mueller polarimetry and can pave the way for future work on label efficient Mueller imaging.

翻译：穆勒矩阵成像为生物医学组织分析提供了丰富且具有物理意义的对比度，但监督学习因缺乏密集标注以及不同样本和采集设置间的强领域漂移而受到阻碍。我们提出MuellerPT，一种物理引导的预训练方法，通过预测逐像素4×4穆勒矩阵的Lu-Chipman分解图，学习可迁移的密集表示。为扩展预训练规模，我们构建了新的大规模多光谱动物偏振器官数据集（MAP-Org）。预训练编码器配以分割头，用于羊脑灰质与白质分割，并使用分类头进行结直肠癌与非癌分类。分割与分类均在少样本学习场景下评估。在分割任务中，与未预训练模型相比，MuellerPT提升了标签效率和跨样本迁移能力，当使用5%训练数据时，相比于从零训练的基线获得了超过20%的绝对DICE增益。在分类任务中，MuellerPT同样提高了标签效率，当使用1%训练数据时，总体准确率较基线提升8%。我们通过定性评估其在离体人食管样本上预测的Lu-Chipman图，证明了MuellerPT对领域漂移的鲁棒性。这些结果表明，预测Lu-Chipman分解是从穆勒偏振测量中进行稳健生物医学推断的有效且实用的预任务，可为未来标签高效的穆勒成像研究铺平道路。