Search Results for author: Mannudeep K. Kalra

Found 17 papers, 9 papers with code

X-ray Dissectography Improves Lung Nodule Detection

no code implementations24 Mar 2022 Chuang Niu, Giridhar Dasegowda, Pingkun Yan, Mannudeep K. Kalra, Ge Wang

Although radiographs are the most frequently used worldwide due to their cost-effectiveness and widespread accessibility, the structural superposition along the x-ray paths often renders suspicious or concerning lung nodules difficult to detect.

Lung Nodule Detection

Deep Interactive Denoiser (DID) for X-Ray Computed Tomography

no code implementations30 Nov 2020 Ti Bai, Biling Wang, Dan Nguyen, Bao Wang, Bin Dong, Wenxiang Cong, Mannudeep K. Kalra, Steve Jiang

However, there exists two challenges regarding the DL-based denoisers: 1) a trained model typically does not generate different image candidates with different noise-resolution tradeoffs which sometimes are needed for different clinical tasks; 2) the model generalizability might be an issue when the noise level in the testing images is different from that in the training dataset.

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 Management +2

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 Segmentation

Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment

no code implementations8 Jul 2020 Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Eli Gibson, R. S. Vishwanath, Abishek Balachandran, James M. Balter, Yue Cao, Ramandeep Singh, Subba R. Digumarthy, Mannudeep K. Kalra, Sasa Grbic, Dorin Comaniciu

In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e. g., by 8% to 0. 91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs.

Anatomy Classification +1

Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment

no code implementations18 Jun 2019 Florin C. Ghesu, Bogdan Georgescu, Eli Gibson, Sebastian Guendel, Mannudeep K. Kalra, Ramandeep Singh, Subba R. Digumarthy, Sasa Grbic, Dorin Comaniciu

We argue that explicitly learning the classification uncertainty as an orthogonal measure to the predicted output, is essential to account for the inherent variability characteristic of this data.

Classification General Classification

Knowledge-based Analysis for Mortality Prediction from CT Images

1 code implementation20 Feb 2019 Hengtao Guo, Uwe Kruger, Ge Wang, Mannudeep K. Kalra, Pingkun Yan

Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality.

Clinical Knowledge Lung Cancer Diagnosis +1

Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising

1 code implementation17 Jan 2019 Fenglei Fan, Hongming Shan, Mannudeep K. Kalra, Ramandeep Singh, Guhan Qian, Matthew Getzin, Yueyang Teng, Juergen Hahn, Ge Wang

Inspired by complexity and diversity of biological neurons, our group proposed quadratic neurons by replacing the inner product in current artificial neurons with a quadratic operation on input data, thereby enhancing the capability of an individual neuron.

Image Denoising

Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?

1 code implementation8 Nov 2018 Hongming Shan, Atul Padole, Fatemeh Homayounieh, Uwe Kruger, Ruhani Doda Khera, Chayanin Nitiwarangkul, Mannudeep K. Kalra, Ge Wang

Here we design a novel neural network architecture for low-dose CT (LDCT) and compare it with commercial iterative reconstruction methods used for standard of care CT.

Denoising Image Reconstruction

Hybrid deep neural networks for all-cause Mortality Prediction from LDCT Images

no code implementations19 Oct 2018 Pingkun Yan, Hengtao Guo, Ge Wang, Ruben De Man, Mannudeep K. Kalra

In this paper, we propose a deep learning based method, which takes both chest LDCT image patches and coronary artery calcification risk scores as input, for direct prediction of mortality risk of lung cancer subjects.

Lung Cancer Diagnosis Mortality Prediction

3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

no code implementations15 Feb 2018 Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Kruger, Mannudeep K. Kalra, Ling Sun, Wenxiang Cong, Ge Wang

Based on the transfer learning from 2D to 3D, the 3D network converges faster and achieves a better denoising performance than that trained from scratch.

Computed Tomography (CT) Denoising +2

Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

9 code implementations3 Aug 2017 Qingsong Yang, Pingkun Yan, Yanbo Zhang, Hengyong Yu, Yongyi Shi, Xuanqin Mou, Mannudeep K. Kalra, Ge Wang

In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity.

Generative Adversarial Network Image Denoising

CT Image Denoising with Perceptive Deep Neural Networks

no code implementations22 Feb 2017 Qingsong Yang, Pingkun Yan, Mannudeep K. Kalra, Ge Wang

Reduction of radiation dose associated with CT can increase noise and artifacts, which can adversely affect diagnostic confidence.

Image Denoising

Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

1 code implementation1 Feb 2017 Hu Chen, Yi Zhang, Mannudeep K. Kalra, Feng Lin, Yang Chen, Peixi Liao, Jiliu Zhou, Ge Wang

Given the potential X-ray radiation risk to the patient, low-dose CT has attracted a considerable interest in the medical imaging field.

Lesion Detection

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