20.500.12556/RUL-178287 DentAssignNet assignment network for dental cast labeling in the presence of dental abnormalities This study focuses on the challenging problem of labeling a collection of objects with inherent morphological and positional dependencies, where instances may be missing or duplicated. We integrate principles of assignment theory in the design of a convolutional neural network to find the optimal label set given pairwise geometrical features extracted from the candidate objects. The objective function aims to minimize the distance between the one-hot encoded labels of the objects and the scores produced by the model, with added emphasis on the scores corresponding to the optimal assignment plan. We tested our solution in the dental domain on the task of finding the teeth labels given a set of candidate instances. The study database included 1200 dental casts of upper and lower jaws from 600 patients. The model reached identification accuracies of 0.952 and 0.968 for the lower and upper jaws, respectively. Moreover, we presented a solution for generating teeth candidates using a multi-step pipeline consisting of coarse and fine segmentations. The algorithm was tested on a database consisting of 600 dental casts, reaching an F1 score of 0.968. medical image analysis assignment theory dental cast geometry labeling deep neural networks analiza medicinskih slik teorija dodeljevanje zobni odlitek geometrija označevanje globoke nevronske mreže true false true Angleški jezik Slovenski jezik Članek v reviji 2026-01-22 13:18:11 2026-01-22 13:18:14 2026-01-23 04:38:07 0000-00-00 00:00:00 2025 0 0 Str. 4981-4990 no. 9 Vol. 29 Jul. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.93:61 2168-2208 10.1109/JBHI.2025.3549685 230531587 DentAssignNet_Assignment_Network_for_Dental_Cast_Labeling_in_the_Presence_of_Dental_Abnormalities.pdf DentAssignNet_Assignment_Network_for_Dental_Cast_Labeling_in_the_Presence_of_Dental_Abnormalities.pdf 1 85BDE7DF861B03467759B1C54FE2720B 61c810cdb1db4b6eeb047651521dfea9c92a395b7e33c2653cbb2a6306480d92 a05f664a-f78c-11f0-a1ba-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=225996 https://ieeexplore.ieee.org/document/10935619 1 6eb415e5-f78c-11f0-a1ba-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=225995 Fakulteta za elektrotehniko 0 0 0