Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology.

[BibT_eX]

[DOI]

CoRR, 2024

2023

MedGen: A Python Natural Language Processing Toolkit for Medical Text Processing.

[BibT_eX]

[DOI]

CoRR, 2023

Attention-based 3D convolutional networks for detection of geographic atrophy from optical coherence tomography scans.

[BibT_eX]

[DOI]

Proceedings of the Medical Imaging 2023: Image Processing, 2023

Drusen segmentation in color fundus photographs for drusenoid pigment epithelial detachment patients based on ground-truth derived from SD-OCTs.

[BibT_eX]

[DOI]

Proceedings of the Medical Imaging 2023: Computer-Aided Diagnosis, San Diego, 2023

2022

Robust convolutional neural networks against adversarial attacks on medical images.

[BibT_eX]

[DOI]

Pattern Recognit., 2022

Predicting myocardial infarction through retinal scans and minimal personal information.

[BibT_eX]

[DOI]

Erica Dall' Armellina

Nat. Mach. Intell., 2022

A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography.

[BibT_eX]

[DOI]

Cristina González-Gonzalo

CoRR, 2022

Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning.

[BibT_eX]

[DOI]

Benjamin S. Glicksberg

Proceedings of the Machine Learning in Medical Imaging - 13th International Workshop, 2022

Semi-supervised learning approach for automatic detection of hyperreflective foci in SD-OCT imaging.

[BibT_eX]

[DOI]

Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, 2022

Retinal layer segmentation for age-related macular degeneration patients with 3D-UNet.

[BibT_eX]

[DOI]

Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, 2022

Device specific SD-OCT retinal layer segmentation using cycle-generative adversarial networks in patients with AMD.

[BibT_eX]

[DOI]

Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, 2022

Deep Learning and Ensemble Method for Optic Disc and Cup Segmentation.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2022

Deep learning automated diagnosis and quantitative classification of cataract type and severity: quantifying the effectiveness and usability of deep learning-assisted disease diagnosis models with 14 ophthalmologists and multi-center validations.

[BibT_eX]

[DOI]

Proceedings of the AMIA 2022, 2022

Automated and Accessible Diagnosis of Age-related Macular Degeneration: a Comparative Analysis of the impact of machine learning models in clinical diagnostic Workflows.

[BibT_eX]

[DOI]

Proceedings of the AMIA 2022, 2022

2021

Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration.

[BibT_eX]

[DOI]

Caroline C. W. Klaver

Daniel T. Luttikhuizen

Chantal Cousineau-Krieger

J. Am. Medical Informatics Assoc., 2021

Automatic detection of ellipsoid zone loss due to Hydroxychloroquine retinal toxicity from SD-OCT imaging.

[BibT_eX]

[DOI]

Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021

Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS.

[BibT_eX]

[DOI]

Gregory C. Ghahramani

Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021

Deep learning detection of reticular pseudodrusen using multi-modal, multi-task, and multi-attention mechanisms: towards automated and accessible classification of age-related macular degeneration.

[BibT_eX]

[DOI]

Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021

2020

Predicting risk of late age-related macular degeneration using deep learning.

[BibT_eX]

[DOI]

npj Digit. Medicine, 2020

Deep-learning-based prediction of late age-related macular degeneration progression.

[BibT_eX]

[DOI]

Nat. Mach. Intell., 2020

Multi-modal, multi-task, multi-attention (M3) deep learning detection of reticular pseudodrusen: towards automated and accessible classification of age-related macular degeneration.

[BibT_eX]

[DOI]

Caroline C. W. Klaver

Daniel T. Luttikhuizen

Chantal Cousineau-Krieger

CoRR, 2020

Feature-based retinal image registration for longitudinal analysis of patients with age-related macular degeneration.

[BibT_eX]

[DOI]

Proceedings of the Medical Imaging 2020: Image Processing, 2020

Detection of reticular pseudodrusen using deep learning.

[BibT_eX]

[DOI]

Proceedings of the AMIA 2020, 2020

2019

A deep learning approach for automated detection of geographic atrophy from color fundus photographs.

[BibT_eX]

[DOI]

CoRR, 2019

Optic Disc and Cup Segmentation for Glaucoma Characterization Using Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the 32nd IEEE International Symposium on Computer-Based Medical Systems, 2019

A deep learning-based survival model for prediction of progression in late Age-related Macular Degeneration (AMD) from color fundus photographs.

[BibT_eX]

[DOI]

Proceedings of the AMIA 2019, 2019

2018

A multi-task deep learning model for the classification of Age-related Macular Degeneration.

[BibT_eX]

[DOI]

CoRR, 2018

DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs.

[BibT_eX]

[DOI]

CoRR, 2018

Region of Interest Detection in Fundus Images Using Deep Learning and Blood Vessel Information.