Search Results for author: Dong Hye Ye

Found 9 papers, 1 papers with code

Deep learning for automated detection of breast cancer in deep ultraviolet fluorescence images with diffusion probabilistic model

no code implementations1 Jul 2024 Sepehr Salem Ghahfarokhi, Tyrell To, Julie Jorns, Tina Yen, Bing Yu, Dong Hye Ye

In this paper, we apply the DPM to augment the deep ultraviolet fluorescence (DUV) image dataset with an aim to improve breast cancer classification for intraoperative margin assessment.

Breast Cancer Detection

Guided Context Gating: Learning to leverage salient lesions in retinal fundus images

no code implementations19 Jun 2024 Teja Krishna Cherukuri, Nagur Shareef Shaik, Dong Hye Ye

Addressing this limitation, we propose a novel attention mechanism called Guided Context Gating, an unique approach that integrates Context Formulation, Channel Correlation, and Guided Gating to learn global context, spatial correlations, and localized lesion context.

Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images

no code implementations18 Jun 2024 Nagur Shareef Shaik, Teja Krishna Cherukuri, Vince Calhoun, Dong Hye Ye

We achieve this by implementing a diversified attention mechanism known as Spatial Sequence Attention (SSA) which is designed to extract and emphasize significant feature representations from structural MRI (sMRI).

Transfer Learning

Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging

1 code implementation24 Oct 2022 David Helminiak, Hang Hu, Julia Laskin, Dong Hye Ye

Mass Spectrometry Imaging (MSI), using traditional rectilinear scanning, takes hours to days for high spatial resolution acquisitions.

regression

Age-Conditioned Synthesis of Pediatric Computed Tomography with Auxiliary Classifier Generative Adversarial Networks

no code implementations31 Jan 2020 Chi Nok Enoch Kan, Najibakram Maheenaboobacker, Dong Hye Ye

Deep learning is a popular and powerful tool in computed tomography (CT) image processing such as organ segmentation, but its requirement of large training datasets remains a challenge.

Computed Tomography (CT) Generative Adversarial Network +1

Deep Back Projection for Sparse-View CT Reconstruction

no code implementations6 Jul 2018 Dong Hye Ye, Gregery T. Buzzard, Max Ruby, Charles A. Bouman

Filtered back projection (FBP) is a classical method for image reconstruction from sinogram CT data.

Image Reconstruction

A Framework for Dynamic Image Sampling Based on Supervised Learning (SLADS)

no code implementations14 Mar 2017 G. M. Dilshan P. Godaliyadda, Dong Hye Ye, Michael D. Uchic, Michael A. Groeber, Gregery T. Buzzard, Charles A. Bouman

In each step of SLADS, the objective is to find the pixel that maximizes the expected reduction in distortion (ERD) given previous measurements.

regression

A Gaussian Mixture MRF for Model-Based Iterative Reconstruction with Applications to Low-Dose X-ray CT

no code implementations12 May 2016 Ruoqiao Zhang, Dong Hye Ye, Debashish Pal, Jean-Baptiste Thibault, Ken D. Sauer, Charles A. Bouman

In this paper, we present a novel Gaussian mixture Markov random field model (GM-MRF) that can be used as a very expressive prior model for inverse problems such as denoising and reconstruction.

Image Denoising

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