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Found 16 papers, 7 papers with code
TANet: Towards Fully Automatic Tooth Arrangement
Determining optimal target tooth arrangements is a key step of treatment planning in digital orthodontics.
SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields
Cone beam computed tomography (CBCT) has been widely used in clinical practice, especially in dental clinics, while the radiation dose of X-rays when capturing has been a long concern in CBCT imaging.
ToothInpaintor: Tooth Inpainting from Partial 3D Dental Model and 2D Panoramic Image
In this paper, we propose a neural network, called ToothInpaintor, that takes as input a partial 3D dental model and a 2D panoramic image and reconstructs the full tooth model with high-quality root(s).
Class-attention Video Transformer for Engagement Intensity Prediction
In order to deal with variant-length long videos, prior works extract multi-modal features and fuse them to predict students' engagement intensity.
Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites
In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the storage cost and privacy restriction.
Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation
The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i. e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation.
WSEBP: A Novel Width-depth Synchronous Extension-based Basis Pursuit Algorithm for Multi-Layer Convolutional Sparse Coding
Specifically, we first propose a novel width-depth synchronous extension-based basis pursuit (WSEBP) algorithm which solves the ML-CSC problem without the limitation of the number of iterations compared to the SOTA algorithms and maximizes the performance by an effective initialization in each layer.
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning
Specifically, the backbone network is firstly trained with a multi-style and multi-view unsupervised self-learning scheme for the embedding of invariant features to various vendor-styles.
Dense Representative Tooth Landmark/axis Detection Network on 3D Model
Results show that our method can produce tooth landmarks with high accuracy.
Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection
Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis.
TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation
State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation.
Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training
The global shape constraint is the inherent property of anatomical landmarks that provides valuable guidance for more consistent pseudo labelling of the unlabeled data, which is ignored in the previously semi-supervised methods.
TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation
State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation.
Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes
We propose an unsupervised learning framework with the pretext task of finding dense correspondences between point cloud shapes from the same category based on the cycle-consistency formulation.
Unsupervised Learning of Intrinsic Structural Representation Points
The 3D structure points produced by our method encode the shape structure intrinsically and exhibit semantic consistency across all the shape instances with similar structures.
ToothNet: Automatic Tooth Instance Segmentation and Identification From Cone Beam CT Images
To the best of our knowledge, our method is the first to use neural networks to achieve automatic tooth segmentation and identification from CBCT images.
