Accurate cell detection in multiplex immunofluorescence (mIF) is crucial for quantifying and analyzing the spatial distribution of complex cellular patterns within the tumor microenvironment. Despite its importance, cell detection in mIF is challenging, primarily due to difficulties obtaining comprehensive annotations. To address the challenge of limited and unevenly distributed annotations, we introduced a streamlined semi-supervised approach that effectively leveraged partially pathologist-annotated single-cell data in multiplexed images across different cancer types. We assessed three leading object detection models, Faster R-CNN, YOLOv5s, and YOLOv8s, with partially annotated data, selecting YOLOv8s for optimal performance. This model was subsequently used to generate pseudo labels, which enriched our dataset by adding more detected labels than the original partially annotated data, thus increasing its generalization and the comprehensiveness of cell detection. By fine-tuning the detector on the original dataset and the generated pseudo labels, we tested the refined model on five distinct cancer types using fully annotated data by pathologists. Our model achieved an average precision of 90.42%, recall of 85.09%, and an F1 Score of 84.75%, underscoring our semi-supervised model's robustness and effectiveness. This study contributes to analyzing multiplexed images from different cancer types at cellular resolution by introducing sophisticated object detection methodologies and setting a novel approach to effectively navigate the constraints of limited annotated data with semi-supervised learning.
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