2024-01-23-chen24b.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
Model Adaptive Tooth Segmentation	Automatic 3-dimensional tooth segmentation on intraoral scans (IOS) plays a pivotal role in computer-aided orthodontic treatments. In practice, deploying existing well-trained models to different medical centers suffers from two main problems: (1) the data distribution shifts between existing and new centers, (2) the data in the existing center is usually not allowed to share while annotating additional data in the new center is time-consuming and expensive. In this paper, we propose a Model Adaptive Tooth Segmentation (MATS) framework to alleviate these issues. Taking the trained model from a source center as input, MATS adapts it to different target centers without data transmission or additional annotations, as inspired by the source data-free domain adaptation (SFDA) paradigm. The model adaptation in MATS is realized by a tooth-level feature prototype learning module, a progressive pseudo-labeling module and a tooth-prior regularized information maximization loss. Experiments on a dataset with tooth abnormalities and a real-world cross-center dataset show that MATS can consistently surpass existing baselines. The effectiveness is further verified with extensive ablation studies and statistical analysis, demonstrating its applicability for privacy-preserving tooth segmentation in real-world digital dentistry.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chen24b	0	Model Adaptive Tooth Segmentation	775	798	775-798	775	false	Chen, Ruizhe and Yang, Jianfei and FENG, YANG and Hao, Jin and Liu, Zuozhu	given family Ruizhe Chen given family Jianfei Yang given family YANG FENG given family Jin Hao given family Zuozhu Liu	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/chen24b/chen24b.pdf