most citedMachine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications

276 citations · 317 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV2021★ 37 cited

Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning

Lequan Yu, Zhicheng Zhang, Xiaomeng Li +3

The presence of metallic implants often introduces severe metal artifacts in the X-ray CT images, which could adversely influence clinical diagnosis or dose calculation in radiatio…

physics.med-ph2021★ 2 cited

Quantitative Parametric Mapping of Tissues Properties from Standard Magnetic Resonance Imaging Enabled by Deep Learning

Yan Wu, Yajun Ma, Youngwook Kee +10

Magnetic resonance imaging (MRI) offers superior soft tissue contrast and is widely used in biomedicine. However, conventional MRI is not quantitative, which presents a bottleneck…

eess.IV2019★ 276 cited

Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications

Hyunseok Seo, Masoud Badiei Khuzani, Varun Vasudevan +5

In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this re…

cs.LG2019★ 1 cited

On Sample Complexity of Projection-Free Primal-Dual Methods for Learning Mixture Policies in Markov Decision Processes

Masoud Badiei Khuzani, Varun Vasudevan, Hongyi Ren +1

We study the problem of learning policy of an infinite-horizon, discounted cost, Markov decision process (MDP) with a large number of states. We compute the actions of a policy tha…

cs.LG2019★ 1 cited

A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning

Masoud Badiei Khuzani, Hongyi Ren, Md Tauhidul Islam +1

We propose a novel data-driven method to learn a mixture of multiple kernels with random features that is certifiabaly robust against adverserial inputs. Specifically, we consider…