-
Article
Open AccessNeuron-level explainable AI for Alzheimer’s Disease assessment from fundus images
Alzheimer’s Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina ...
-
Article
Open AccessDeep learning predicts prevalent and incident Parkinson’s disease from UK Biobank fundus imaging
Parkinson’s disease is the world’s fastest-growing neurological disorder. Research to elucidate the mechanisms of Parkinson’s disease and automate diagnostics would greatly improve the treatment of patients wi...
-
Protocol
Analysis and Visualization of Single-Cell Sequencing Data with Scanpy and MetaCell: A Tutorial
The emergence and development of single-cell RNA sequencing (scRNA-seq) techniques enable researchers to perform large-scale analysis of the transcriptomic profiling at cell-specific resolution. Unsupervised c...
-
Article
Open AccessEthnic disparity in diagnosing asymptomatic bacterial vaginosis using machine learning
While machine learning (ML) has shown great promise in medical diagnostics, a major challenge is that ML models do not always perform equally well among ethnic groups. This is alarming for women’s health, as t...
-
Chapter and Conference Paper
DOMINO++: Domain-Aware Loss Regularization for Deep Learning Generalizability
Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model’s training data. DL models that p...
-
Article
Open AccessIdentify diabetic retinopathy-related clinical concepts and their attributes using transformer-based natural language processing methods
Diabetic retinopathy (DR) is a leading cause of blindness in American adults. If detected, DR can be treated to prevent further damage causing blindness. There is an increasing interest in develo** artificia...
-
Chapter and Conference Paper
DOMINO: Domain-Aware Model Calibration in Medical Image Segmentation
Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applications. Unfortunately, modern dee...
-
Article
Open AccessModular machine learning for Alzheimer's disease classification from retinal vasculature
Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, curren...
-
Article
Open AccessPublisher Correction: Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
-
Article
Open AccessAutomatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning
The choroid layer is a vascular layer in human retina and its main function is to provide oxygen and support to the retina. Various studies have shown that the thickness of the choroid layer is correlated with...
-
Chapter and Conference Paper
Abdominal Adipose Tissue Segmentation in MRI with Double Loss Function Collaborative Learning
Deep learning has shown promising progress in computer-aided medical image diagnosis in recent years, such adipose tissue segmentation. Generally, training a high-performance deep segmentation model requires a...
-
Chapter and Conference Paper
CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation
Recently, deep neural networks have demonstrated comparable and even better performance with board-certified ophthalmologists in well-annotated datasets. However, the diversity of retinal imaging devices poses...
-
Chapter
Deep Spatial-Temporal Convolutional Neural Networks for Medical Image Restoration
Computed tomography perfusion (CTP) facilitates low-cost diagnosis and treatment of acute stroke. Cine scanning allows users to visualize brain anatomy and blood flow in virtually live time. However, effectiv...
-
Chapter and Conference Paper
Neural Network Evolution Using Expedited Genetic Algorithm for Medical Image Denoising
Convolutional neural networks offer state-of-the-art performance for medical image denoising. However, their architectures are manually designed for different noise types. The realistic noise in medical images...
-
Chapter and Conference Paper
Multi-task Fundus Image Quality Assessment via Transfer Learning and Landmarks Detection
The quality of fundus images is critical for diabetic retinopathy diagnosis. The evaluation of fundus image quality can be affected by several factors, including image artifact, clarity, and field definition. ...
-
Chapter and Conference Paper
Correction to: Retinal Microaneurysm Detection Using Clinical Report Guided Multi-Sieving CNN
In the originally published version important references were omitted.
-
Chapter and Conference Paper
Retinal Microaneurysm Detection Using Clinical Report Guided Multi-sieving CNN
Timely detection and treatment of microaneurysms (MA) is a critical step to prevent the development of vision-threatening eye diseases such as diabetic retinopathy. However, detecting MAs in fundus images is a...
-
Chapter and Conference Paper
STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion
Computed tomography perfusion (CTP) is one of the most widely used imaging modality for cerebrovascular disease diagnosis and treatment, especially in emergency situations. While cerebral CTP is capable of qua...
-
Chapter and Conference Paper
Efficient 4D Non-local Tensor Total-Variation for Low-Dose CT Perfusion Deconvolution
Tensor total variation deconvolution has been recently proposed as a robust framework to accurately estimate the hemodynamic parameters in low-dose CT perfusion by fusing the local anatomical structure correla...
-
Chapter and Conference Paper
Fast Preconditioning for Accelerated Multi-contrast MRI Reconstruction
Real-time reconstruction in multi-contrast magnetic resonance imaging (MC-MRI) is very challenging due to the slow scanning and reconstruction process. In this study, we propose a novel algorithm to accelerate...
