Search Results for author: Blake E. Dewey

Found 11 papers, 2 papers with code

Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion

no code implementations3 Dec 2023 Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Dzung L. Pham, Aaron Carass, Jerry L. Prince

Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation.

Lesion Segmentation Segmentation

Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

no code implementations31 Oct 2023 Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Savannah P. Hays, Dzung L. Pham, Jerry L. Prince, Aaron Carass

Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation.

Domain Generalization Lesion Segmentation

A latent space for unsupervised MR image quality control via artifact assessment

no code implementations1 Feb 2023 Lianrui Zuo, Yuan Xue, Blake E. Dewey, Yihao Liu, Jerry L. Prince, Aaron Carass

Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images.

Contrastive Learning

Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis

no code implementations4 Mar 2021 Dzung L. Pham, Yi-Yu Chou, Blake E. Dewey, Daniel S. Reich, John A. Butman, Snehashis Roy

Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images.

Classification General Classification

Self domain adapted network

1 code implementation7 Jul 2020 Yufan He, Aaron Carass, Lianrui Zuo, Blake E. Dewey, Jerry L. Prince

However, training a model for each target domain is time consuming and computationally expensive, even infeasible when target domain data are scarce or source data are unavailable due to data privacy.

Self-Supervised Learning Unsupervised Domain Adaptation

Evaluating the Impact of Intensity Normalization on MR Image Synthesis

1 code implementation11 Dec 2018 Jacob C. Reinhold, Blake E. Dewey, Aaron Carass, Jerry L. Prince

Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image.

Image Generation Imputation

Self Super-Resolution for Magnetic Resonance Images using Deep Networks

no code implementations26 Feb 2018 Can Zhao, Aaron Carass, Blake E. Dewey, Jerry L. Prince

This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image.


Cannot find the paper you are looking for? You can Submit a new open access paper.