In recent years, numerous domain adaptive strategies have been proposed to help deep learning models overcome the challenges posed by domain shift. However, even unsupervised domain adaptive strategies still require a large amount of target data. Medical imaging datasets are often characterized by class imbalance and scarcity of labeled and unlabeled data. Few-shot domain adaptive object detection (FSDAOD) addresses the challenge of adapting object detectors to target domains with limited labeled data. Existing works struggle with randomly selected target domain images that may not accurately represent the real population, resulting in overfitting to small validation sets and poor generalization to larger test sets. Medical datasets exhibit high class imbalance and background similarity, leading to increased false positives and lower mean Average Precision (map) in target domains. To overcome these challenges, we propose a novel FSDAOD strategy for microscopic imaging. Our contributions include a domain adaptive class balancing strategy for few-shot scenarios, multi-layer instance-level inter and intra-domain alignment to enhance similarity between class instances regardless of domain, and an instance-level classification loss applied in the middle layers of the object detector to enforce feature retention necessary for correct classification across domains. Extensive experimental results with competitive baselines demonstrate the effectiveness of our approach, achieving state-of-the-art results on two public microscopic datasets. Code available at https://github.co/intelligentMachinesLab/few-shot-domain-adaptive-microscopy

翻译：近年来，众多域自适应策略被提出以帮助深度学习模型克服域偏移带来的挑战。然而，即使是无监督域自适应策略仍需要大量目标域数据。医学影像数据集通常具有类别不平衡以及标注与未标注数据稀缺的特点。少样本域自适应目标检测旨在解决目标域标注数据有限条件下目标检测器的适应问题。现有方法在处理随机选取的目标域图像时存在局限，这些图像可能无法准确代表真实数据分布，导致模型在小规模验证集上过拟合，并在更大测试集上泛化性能不佳。医学数据集呈现高度类别不平衡和背景相似性，导致目标域中误检率增加、平均精度均值下降。为克服这些挑战，我们提出了一种面向显微成像的新型少样本域自适应目标检测策略。我们的贡献包括：针对少样本场景的域自适应类别平衡策略；通过多层实例级域间与域内对齐增强跨域类别实例的相似性；以及在目标检测器中间层施加实例级分类损失，以强制模型保留跨域正确分类所需的特征表征。大量实验结果表明，该方法在多个竞争性基线对比中展现出优越性能，在两个公开显微数据集上取得了最先进的结果。代码发布于 https://github.co/intelligentMachinesLab/few-shot-domain-adaptive-microscopy