2024-01-23-hwang24a.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
Semi-supervised Learning with Contrastive and Topology Losses for Catheter Segmentation and Misplacement Prediction	Chest X-ray images are often used to determine the proper placement of catheters, as incorrect placement can lead to severe complications. With the advent of deep learning, computer-aided detection methods have been developed to assist radiologists in identifying catheter misplacement by detecting and highlighting the catheter’s path. However, obtaining large, pixel-wise labeled datasets can be challenging due to the labor-intensive nature of annotation. To address this issue, we proposed a novel semi-supervised learning method that combines contrastive loss and topology loss. This method takes advantage of the known topological properties of catheters and does not require extensive labeling. We collected 7,378 chest X-ray images from the *****, which were labeled for misplacement of nasogastric and endotracheal tube catheters, and included pixel-wise annotation. Moreover, the CLiP dataset was used as an unlabeled dataset for semi-supervised learning. We used a hybrid U-Net architecture with an added classification head to perform simultaneous segmentation of the catheter and misplacement classification. Our model achieved an average AUC of 0.977 for classification and a average Dice score of 0.598 for segmentation.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	hwang24a	0	Semi-supervised Learning with Contrastive and Topology Losses for Catheter Segmentation and Misplacement Prediction	1239	1253	1239-1253	1239	false	Hwang, Tianyu and Wang, Chih-Hung and Roth, Holger R and Yang, Dong and Zhao, Can and Huang, Chien-Hua and Wang, Weichung	given family Tianyu Hwang given family Chih-Hung Wang given family Holger R Roth given family Dong Yang given family Can Zhao given family Chien-Hua Huang given family Weichung Wang	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/hwang24a/hwang24a.pdf