Machine Learning

Oral Presentation in 32nd National Conference and the 10th International Conference on Iranian Biomedical Engineering on Ensemble Learning-Based Surrogate Models for Non-Invasive Estimation of Corneal Mechanical Properties

A neural network-based inverse model was developed to predict Ogden material parameters from corneal apex displacement data.

Oral Presentation in 2nd Regional Symposium on Science and Technology on Neural Network-Based Inverse Model for Non-Invasive Estimation of Corneal Mechanical Properties

Oral Presentation in 7th International Conference on Preclinical Imaging Applications on Deep Learning-Based Quantitative Assessment of Pulmonary Vascular Changes Using Histological Microscopy Images

The objective of this research is to develop and validate a deep learning model for predicting the hypertrophy index from histological microscopy images of pulmonary circulation vessels.