output
20202024
most citedExplainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?

60 citations

5 papers

stat.ML2024★ 21 cited

Conditional score-based diffusion models for solving inverse problems in mechanics

Agnimitra Dasgupta, Harisankar Ramaswamy, Javier Murgoitio-Esandi +5

We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a s…

eess.IV2022★ 12 cited

3D Brain and Heart Volume Generative Models: A Survey

Yanbin Liu, Girish Dwivedi, Farid Boussaid +1

Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capabil…

eess.IV2022★ 18 cited

Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images

Yanbin Liu, Girish Dwivedi, Farid Boussaid +3

The generation of three-dimensional (3D) medical images has great application potential since it takes into account the 3D anatomical structure. Two problems prevent effective trai…

q-bio.QM2021★ 60 cited

Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?

Isaac Ronald Ward, Ling Wang, Juan lu +3

Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) mode…

cs.LG2020★ 14 cited

Coronary Artery Segmentation from Intravascular Optical Coherence Tomography Using Deep Capsules

Arjun Balaji, Lachlan Kelsey, Kamran Majeed +2

The segmentation and analysis of coronary arteries from intravascular optical coherence tomography (IVOCT) is an important aspect of diagnosing and managing coronary artery disease…