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Author Correction: Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

NPJ digital medicine, 2024-09, Vol.7 (1), p.240-1, Article 240

2024

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Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities

IEEE transactions on pattern analysis and machine intelligence, 2024-04, Vol.PP, p.1-19

2024

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CROSS-MODAL VALIDATION OF AN ARTIFICIAL INTELLIGENCE VIDEO-BASED APPROACH FOR THE AUTOMATED RISK STRATIFICATION OF AORTIC STENOSIS

Journal of the American College of Cardiology, 2024-04, Vol.83 (13), p.1418-1418

2024

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Abstract 18776: ECG-GPT: Automated Complete Diagnosis Generation From ECG Images Using Novel Vision-Text Transformer Model

Circulation (New York, N.Y.), 2023-11, Vol.148 (Suppl_1)

2023

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Report on UG 2 + challenge Track 1: Assessing algorithms to improve video object detection and classification from unconstrained mobility platforms

Computer vision and image understanding, 2021-12, Vol.213, p.103297, Article 103297

2021

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A joint perspective towards image super-resolution: Unifying external- and self-examples

IEEE Winter Conference on Applications of Computer Vision, 2014, p.596-603

2014

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IC‐P‐025: FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer's & dementia, 2018-07, Vol.14 (7S_Part_1), p.P30-P31

2018

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P2‐073: FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer's & dementia, 2018-07, Vol.14 (7S_Part_12), p.P695-P695

2018

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BIOMETRIC CONTRASTIVE MODELING FOR DATA-EFFICIENT DEEP LEARNING FROM ELECTROCARDIOGRAPHIC IMAGES

Journal of the American College of Cardiology, 2023-03, Vol.81 (8), p.2403-2403

2023

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Abstract 13659: Automated Detection of Aortic Stenosis From Single-View 2-Dimensional Echocardiography Using a Semi-Supervised, Contrastive Learning Approach

Circulation (New York, N.Y.), 2022-11, Vol.146 (Suppl_1)

2022

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One is Not Enough: Parameter-Efficient Fine-tuning with Multiplicative Sparse Factorization

IEEE journal of selected topics in signal processing, 2024-07, p.1-11

2024

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LLM-PBE: Assessing Data Privacy in Large Language Models

Proceedings of the VLDB Endowment, 2024-07, Vol.17 (11), p.3201-3214

2024

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GAN Slimming: All-in-One GAN Compression by a Unified Optimization Framework

Computer Vision - ECCV 2020, 2020, Vol.12349, p.54-73

2020

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DAVID: Dual-Attentional Video Deblurring

2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, p.2365-2374

2020

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Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches

Computer Vision - ECCV 2020, 2020, Vol.12360, p.601-617

2020

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Grounded-Instruct-Pix2Pix: Improving Instruction Based Image Editing with Automatic Target Grounding

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, p.6585-6589

2024

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Detect depression from communication: how computer vision, signal processing, and sentiment analysis join forces

IISE transactions on healthcare systems engineering, 2018-07, Vol.8 (3), p.196-208

2018

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Message from the Program Chairs

2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2020, p.13-14

2020

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QUANTIFICATION OF AMYLOID BURDEN FROM FLORBETAPIR PET WITHOUT USING TARGET AND REFERENCE REGIONS: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer's & dementia, 2018-07, Vol.14 (7), p.P315-P316

2018

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P2‐073: FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer's & dementia, 2018-07, Vol.14 (7S_Part_12)

2018

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P1‐118: QUANTIFICATION OF AMYLOID BURDEN FROM FLORBETAPIR PET WITHOUT USING TARGET AND REFERENCE REGIONS: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer's & dementia, 2018-07, Vol.14 (7S_Part_5), p.P315-P316

2018

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FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer's & dementia, 2018-07, Vol.14 (7), p.P695-P695

2018

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FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer's & dementia, 2018-07, Vol.14 (7), p.P30-P31

2018

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IC‐P‐026: QUANTIFICATION OF AMYLOID BURDEN FROM FLORBETAPIR PET IMAGES WITHOUT USING TARGET AND REFERENCE REGIONS: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer's & dementia, 2018-07, Vol.14 (7S_Part_1), p.P31-P31

2018

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IC‐P‐025: FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer's & dementia, 2018-07, Vol.14 (7S_Part_1)

2018

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Author Correction: Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities

CROSS-MODAL VALIDATION OF AN ARTIFICIAL INTELLIGENCE VIDEO-BASED APPROACH FOR THE AUTOMATED RISK STRATIFICATION OF AORTIC STENOSIS

Abstract 18776: ECG-GPT: Automated Complete Diagnosis Generation From ECG Images Using Novel Vision-Text Transformer Model

Report on UG 2 + challenge Track 1: Assessing algorithms to improve video object detection and classification from unconstrained mobility platforms

A joint perspective towards image super-resolution: Unifying external- and self-examples

IC‐P‐025: FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

P2‐073: FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

BIOMETRIC CONTRASTIVE MODELING FOR DATA-EFFICIENT DEEP LEARNING FROM ELECTROCARDIOGRAPHIC IMAGES

Abstract 13659: Automated Detection of Aortic Stenosis From Single-View 2-Dimensional Echocardiography Using a Semi-Supervised, Contrastive Learning Approach

One is Not Enough: Parameter-Efficient Fine-tuning with Multiplicative Sparse Factorization

LLM-PBE: Assessing Data Privacy in Large Language Models

GAN Slimming: All-in-One GAN Compression by a Unified Optimization Framework

DAVID: Dual-Attentional Video Deblurring

Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches

Grounded-Instruct-Pix2Pix: Improving Instruction Based Image Editing with Automatic Target Grounding

Detect depression from communication: how computer vision, signal processing, and sentiment analysis join forces

Message from the Program Chairs

QUANTIFICATION OF AMYLOID BURDEN FROM FLORBETAPIR PET WITHOUT USING TARGET AND REFERENCE REGIONS: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

P2‐073: FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

P1‐118: QUANTIFICATION OF AMYLOID BURDEN FROM FLORBETAPIR PET WITHOUT USING TARGET AND REFERENCE REGIONS: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

IC‐P‐026: QUANTIFICATION OF AMYLOID BURDEN FROM FLORBETAPIR PET IMAGES WITHOUT USING TARGET AND REFERENCE REGIONS: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

IC‐P‐025: FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

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