ICBME 2025 - Oral Presentation 2
Oral Presentation in _32nd National Conference and the 10th International Conference on Iranian Biomedical Engineering_ on __Ensemble Learning-Based Surrogate Models for …
Ensemble Learning-Based Surrogate Models for Non-Invasive Estimation of Corneal Mechanical Properties
Random Forest, XGBoost, and LightGBM surrogate models predict third-order Ogden corneal parameters from apex displacement data, evaluated with a physics-informed stress-stretch …
ReSIST 2024 - Oral Presentation
Oral Presentation in _2nd Regional Symposium on Science and Technology_ on __Neural Network-Based Inverse Model for Non-Invasive Estimation of Corneal Mechanical Properties__
Neural Network-Based Inverse Model 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.
TPIC 2024 - Oral Presentation
Oral Presentation in _7th International Conference on Preclinical Imaging Applications_ on __Deep Learning-Based Quantitative Assessment of Pulmonary Vascular Changes Using …
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 …