Search Results for author: Kyungsang Kim

Found 10 papers, 2 papers with code

Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19

no code implementations26 Nov 2020 Aoxiao Zhong, Xiang Li, Dufan Wu, Hui Ren, Kyungsang Kim, YoungGon Kim, Varun Buch, Nir Neumark, Bernardo Bizzo, Won Young Tak, Soo Young Park, Yu Rim Lee, Min Kyu Kang, Jung Gil Park, Byung Seok Kim, Woo Jin Chung, Ning Guo, Ittai Dayan, Mannudeep K. Kalra, Quanzheng Li

These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.

Image Retrieval Metric Learning

Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis

1 code implementation26 Sep 2020 Young-Gon Kim, Kyungsang Kim, Dufan Wu, Hui Ren, Won Young Tak, Soo Young Park, Yu Rim Lee, Min Kyu Kang, Jung Gil Park, Byung Seok Kim, Woo Jin Chung, Mannudeep K. Kalra, Quanzheng Li

A segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used, which are the clinical landmarks to separate the upper and lower lungs.

COVID-19 Diagnosis

Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples

no code implementations9 Jun 2019 Dufan Wu, Kuang Gong, Kyungsang Kim, Quanzheng Li

In this paper we proposed a training method which learned denoising neural networks from noisy training samples only.

Image Denoising Medical Image Denoising

Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction

no code implementations5 Oct 2018 Dufan Wu, Kyungsang Kim, Quanzheng Li

The purpose of this work is to reduce the memory and time consumption of the training of the reconstruction networks for CT to make it practical for current hardware, while maintaining the quality of the reconstructed images.

Computed Tomography (CT) Image Reconstruction

End-to-End Abnormality Detection in Medical Imaging

no code implementations ICLR 2018 Dufan Wu, Kyungsang Kim, Bin Dong, Quanzheng Li

To align the acquisition with the annotations made by radiologists in the image domain, a DNN was built as the unrolled version of iterative reconstruction algorithms to map the acquisitions to images, and followed by a 3D convolutional neural network (CNN) to detect the abnormality in the reconstructed images.

Anomaly Detection Computed Tomography (CT) +1

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images

no code implementations17 Dec 2017 Kuang Gong, Jaewon Yang, Kyungsang Kim, Georges El Fakhri, Youngho Seo, Quanzheng Li

With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods.

Image Reconstruction

End-to-end Lung Nodule Detection in Computed Tomography

no code implementations6 Nov 2017 Dufan Wu, Kyungsang Kim, Bin Dong, Georges El Fakhri, Quanzheng Li

With 144 multi-slice fanbeam pro-jections, the proposed end-to-end detector could achieve comparable sensitivity with the reference detector, which was trained and applied on the fully-sampled image data.

Computed Tomography (CT) Fine-tuning +1

Iterative PET Image Reconstruction Using Convolutional Neural Network Representation

1 code implementation9 Oct 2017 Kuang Gong, Jiahui Guan, Kyungsang Kim, Xuezhu Zhang, Georges El Fakhri, Jinyi Qi, Quanzheng Li

An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool.

Denoising Image Reconstruction

A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising

no code implementations11 May 2017 Dufan Wu, Kyungsang Kim, Georges El Fakhri, Quanzheng Li

Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT).

Computed Tomography (CT) Image Denoising

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