Various strategies for label-scarce object detection have been explored by the computer vision research community. These strategies mainly rely on assumptions that are specific to natural images and not directly applicable to the biological and biomedical vision domains. For example, most semi-supervised learning strategies rely on a small set of labeled data as a confident source of ground truth. In many biological vision applications, however, the ground truth is unknown and indirect information might be available in the form of noisy estimations or orthogonal evidence. In this work, we frame a crucial problem in spatial transcriptomics - decoding barcodes from In-Situ-Sequencing (ISS) images - as a semi-supervised object detection (SSOD) problem. Our proposed framework incorporates additional available sources of information into a semi-supervised learning framework in the form of privileged information. The privileged information is incorporated into the teacher's pseudo-labeling in a teacher-student self-training iteration. Although the available privileged information could be data domain specific, we have introduced a general strategy of pseudo-labeling enhanced by privileged information (PLePI) and exemplified the concept using ISS images, as well on the COCO benchmark using extra evidence provided by CLIP.

翻译：计算机视觉研究领域已探索了多种针对标签稀缺目标检测的策略。这些策略主要基于自然图像特有的假设，难以直接适用于生物与生物医学视觉领域。例如，大多数半监督学习策略依赖少量标注数据作为可信的真值来源。然而在许多生物视觉应用中，真值未知，但可能存在噪声估计或正交证据形式的间接信息。本研究将空间转录组学中的关键问题——从原位测序（ISS）图像中解码条形码——构建为半监督目标检测（SSOD）问题。我们提出的框架以特权信息的形式，将额外的可用信息源融入半监督学习框架中。在教师-学生自训练迭代过程中，特权信息被整合到教师的伪标签生成中。尽管可用的特权信息可能具有数据领域特异性，但我们提出了一种由特权信息增强的伪标签（PLePI）通用策略，并通过ISS图像以及基于CLIP提供的额外证据在COCO基准上对该概念进行了实例验证。