A. Killekar

发表

Automated nonlinear registration of coronary PET to CT angiography using pseudo-CT generated from PET with generative adversarial networks

D. Dey, P. Slomka, M. Dweck, 2022, Journal of Nuclear Cardiology.

COVID-19 lesion segmentation using convolutional LSTM for self-attention

D. Dey, G. Pontone, P. Slomka, 2022, Medical Imaging.

Artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary computed tomography angiography using a novel method

D. Dey, S. Achenbach, M. Dweck, 2022, Medical Imaging.

Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images

D. Dey, H. Gransar, P. Slomka, 2022, Circulation. Cardiovascular imaging.

Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.

D. Dey, H. Gransar, P. Slomka, 2022, JACC. Cardiovascular imaging.

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks

D. Dey, G. Pontone, G. Parati, 2022, Journal of medical imaging.

Calcium scoring in low-dose ungated chest CT scans using convolutional long-short term memory networks

D. Dey, T. Bateman, E. Miller, 2022, Medical Imaging.

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

D. Dey, S. Achenbach, H. Gransar, 2022, The Lancet. Digital health.

Two-branch Recurrent Network for Isolating Deepfakes in Videos

Iacopo Masi, Wael AbdAlmageed, Aditya Killekar, 2020, ECCV.

Radiomorphological signs and clinical severity of SARS-CoV-2 lineage B.1.1.7

D. Dey, P. Slomka, B. Merkely, 2021, BJR open.

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks

Piotr J. Slomka, Damini Dey, Gianfranco Parati, 2021, ArXiv.

Automated nonlinear registration of coronary PET to CT angiography using pseudo-CT generated from PET with generative adversarial networks

D. Dey, P. Slomka, M. Dweck, 2022, Journal of Nuclear Cardiology.

Pseudo-contrast cardiac CT angiography derived from non-contrast CT using conditional generative adversarial networks

D. Dey, P. Slomka, C. Kȩpka, 2023, Medical Imaging.

Deep learning-based plaque quantification from coronary computed tomography angiography: external validation and comparison with intravascular ultrasound

D. Dey, S. Achenbach, M. Dweck, 2021, European Heart Journal.

Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events

D. Dey, D. Berman, P. Slomka, 2022, The Journal of Nuclear Medicine.

Deep Learning From Coronary Computed Tomography Angiography for Atherosclerotic Plaque and Stenosis Quantification and Cardiac Risk Prediction

D. Dey, D. Berman, S. Achenbach, 2021, SSRN Electronic Journal.

Direct head-to-head comparison of convolutional long short-term memory and transformer networks for artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary CT angiography

D. Dey, P. Slomka, M. Dweck, 2023, Medical Imaging.

AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging

D. Dey, T. Ruddy, P. Kavanagh, 2024, npj Digit. Medicine.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography

D. Dey, T. Ruddy, P. Kavanagh, 2024, Nature communications.