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Found 6 papers, 6 papers with code
A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans
Tooth segmentation from intraoral scans is a crucial part of digital dentistry.
3D Tooth Mesh Segmentation with Simplified Mesh Cell Representation
Summarizing of the mesh cell/triangle in this manner imposes an implicit structural constraint and makes it difficult to work with multiple resolutions which is done in many point cloud based deep learning algorithms.
Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental Model
We conclude that the segmentation methods can learn a great deal of information from a single 3D tooth point cloud scan under suitable conditions e. g. data augmentation.
Global and Local Interpretation of black-box Machine Learning models to determine prognostic factors from early COVID-19 data
We explore one of the most recent techniques called symbolic metamodeling to find the mathematical expression of the machine learning models for COVID-19.
Liver Fibrosis and NAS scoring from CT images using self-supervised learning and texture encoding
The severity and treatment of NAFLD is determined by NAFLD Activity Scores (NAS)and liver fibrosis stage, which are usually obtained from liver biopsy.
Deep Learning based NAS Score and Fibrosis Stage Prediction from CT and Pathology Data
In this work, we propose a novel method to automatically predict NAS score and fibrosis stage from CT data that is non-invasive and inexpensive to obtain compared with liver biopsy.
