no code implementations • 2 Aug 2023 • Ziyi Huang, Hongshan Liu, Haofeng Zhang, Xueshen Li, Haozhe Liu, Fuyong Xing, Andrew Laine, Elsa Angelini, Christine Hendon, Yu Gan
One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a set of expert-level annotations while attaining good segmentation performance.
no code implementations • 22 Jul 2023 • Xueshen Li, Hongshan Liu, Xiaoyu Song, Brigitta C. Brott, Silvio H. Litovsky, Yu Gan
There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease.
no code implementations • 20 Jul 2023 • Xueshen Li, Zhenxing Dong, Hongshan Liu, Jennifer J. Kang-Mieler, Yuye Ling, Yu Gan
Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology.
no code implementations • 24 Jan 2023 • Xueshen Li, Yu Gan, David Duan, Xiao Yang
In this paper, we develop algorithms to detect and measure human gastric peristalsis (contraction wave) using video sequences acquired by MCCE.
no code implementations • 12 Nov 2022 • Hongshan Liu, Xueshen Li, Abdul Latif Bamba, Xiaoyu Song, Brigitta C. Brott, Silvio H. Litovsky, Yu Gan
With the calibrated confidence score of each prediction, we lower the uncertainty of predictions in calcification detection.
no code implementations • 12 Nov 2022 • Xueshen Li, Hongshan Liu, Xiaoyu Song, Brigitta C. Brott, Silvio H. Litovsky, Yu Gan
Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD).
no code implementations • 9 Jun 2022 • Ziyi Huang, Yu Gan, Theresa Lye, Yanchen Liu, Haofeng Zhang, Andrew Laine, Elsa Angelini, Christine Hendon
To lessen the need for pixel-wise labeling, we develop a two-stage deep learning framework for cardiac adipose tissue segmentation using image-level annotations on OCT images of human cardiac substrates.
no code implementations • 26 Apr 2022 • Hongshan Liu, Colin Aderon, Noah Wagon, Abdul Latif Bamba, Xueshen Li, Huapu Liu, Steven MacCall, Yu Gan
Identifying players from videos in each play is also essential for the indexing of player participation.
no code implementations • 25 Apr 2022 • Xueshen Li, Shengting Cao, Hongshan Liu, Xinwen Yao, Brigitta C. Brott, Silvio H. Litovsky, Xiaoyu Song, Yuye Ling, Yu Gan
There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging.
no code implementations • 23 Feb 2022 • Yaoqi Tang, Yufan Li, Hongshan Liu, Jiaxuan Li, Peiyao Jin, Yu Gan, Yuye Ling, Yikai Su
To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract features from a small amount of training images for further inpainting and to regularize the image after the multi-scale image fusion, while convolutional neural network (CNN) is employed to enhance the image quality.
no code implementations • 5 Nov 2021 • Zhenxing Dong, Hong Cao, Wang Shen, Yu Gan, Yuye Ling, Guangtao Zhai, Yikai Su
In particular, we propose to use a convolutional neural network (CNN) to learn the cutoff frequency of real-world degradation process.
1 code implementation • 9 Feb 2021 • Jiaxuan Li, Peiyao Jin, Jianfeng Zhu, Haidong Zou, Xun Xu, Min Tang, Minwen Zhou, Yu Gan, Jiangnan He, Yuye Ling, Yikai Su
An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma.
Medical Image Segmentation Retinal OCT Layer Segmentation +1
no code implementations • 31 Jan 2021 • Ziyi Huang, Haofeng Zhang, Andrew Laine, Elsa Angelini, Christine Hendon, Yu Gan
Supervised deep learning performance is heavily tied to the availability of high-quality labels for training.
no code implementations • 2 May 2019 • Yu Gan, Yanqi Zhang, Kelvin Hu, Dailun Cheng, Yuan He, Meghna Pancholi, Christina Delimitrou
We show that Seer correctly anticipates QoS violations 91% of the time, and avoids the QoS violation to begin with in 84% of cases.