2024-12-23-shokrollahi24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Advancing Multiplex Immunofluorescence Imaging Cell Detection using Semi-Supervised Learning with Pseudo-Labeling	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 modelś 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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	shokrollahi24a	0	Advancing Multiplex Immunofluorescence Imaging Cell Detection using Semi-Supervised Learning with Pseudo-Labeling	1448	1461	1448-1461	1448	false	Shokrollahi, Yasin and Gonzales, Karina Pinao and Salvatierra, Maria Esther and Castillo, Simon P. and Gautam, Tanishq and Chen, Pingjun and Rodriguez, B. Leticia and Ranjbar, Sara and Team, Patient Mosaic and Soto, Luisa Solis and Yuan, Yinyin and Pan, Xiaoxi	given family Yasin Shokrollahi given family Karina Pinao Gonzales given family Maria Esther Salvatierra given family Simon P. Castillo given family Tanishq Gautam given family Pingjun Chen given family B. Leticia Rodriguez given family Sara Ranjbar given family Patient Mosaic Team given family Luisa Solis Soto given family Yinyin Yuan given family Xiaoxi Pan	2024-12-23		Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning	250	inproceedings	date-parts 2024 12 23	https://raw.githubusercontent.com/mlresearch/v250/main/assets/shokrollahi24a/shokrollahi24a.pdf