Multi-task deep learning for medical image computing and analysis: A review
The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …
conventional deep learning models are constructed for a single specific task, multi-task deep …
Automated detection and diagnosis of diabetic retinopathy: A comprehensive survey
V Lakshminarayanan, H Kheradfallah, A Sarkar… - Journal of …, 2021 - mdpi.com
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few
years, artificial intelligence (AI) based approaches have been used to detect and grade DR …
years, artificial intelligence (AI) based approaches have been used to detect and grade DR …
A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning
This survey aims to identify commonly used methods, datasets, future trends, knowledge
gaps, constraints, and limitations in the field to provide an overview of current solutions used …
gaps, constraints, and limitations in the field to provide an overview of current solutions used …
Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy
X Huang, H Wang, C She, J Feng, X Liu, X Hu… - Frontiers in …, 2022 - frontiersin.org
Deep learning evolves into a new form of machine learning technology that is classified
under artificial intelligence (AI), which has substantial potential for large-scale healthcare …
under artificial intelligence (AI), which has substantial potential for large-scale healthcare …
Fine-grained visual classification with high-temperature refinement and background suppression
Fine-grained visual classification is a challenging task due to the high similarity between
categories and distinct differences among data within one single category. To address the …
categories and distinct differences among data within one single category. To address the …
Lvm-med: Learning large-scale self-supervised vision models for medical imaging via second-order graph matching
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited
annotated samples has remained an open challenge for medical imaging data. While pre …
annotated samples has remained an open challenge for medical imaging data. While pre …
DR-GAN: conditional generative adversarial network for fine-grained lesion synthesis on diabetic retinopathy images
Diabetic retinopathy (DR) is a complication of diabetes that severely affects eyes. It can be
graded into five levels of severity according to international protocol. However, optimizing a …
graded into five levels of severity according to international protocol. However, optimizing a …
Explainable diabetic retinopathy detection and retinal image generation
Though deep learning has shown successful performance in classifying the label and
severity stage of certain diseases, most of them give few explanations on how to make …
severity stage of certain diseases, most of them give few explanations on how to make …
iERM: An interpretable deep learning system to classify epiretinal membrane for different optical coherence tomography devices: A multi-center analysis
Background: Epiretinal membranes (ERM) have been found to be common among
individuals> 50 years old. However, the severity grading assessment for ERM based on …
individuals> 50 years old. However, the severity grading assessment for ERM based on …
An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images
AM Dayana, WRS Emmanuel - Multimedia Tools and Applications, 2022 - Springer
Diabetic Retinopathy (DR) is one of the long-lasting Diabetic retinal disorders that leads to
vision impairment eventually blindness in most of the working-age population. The process …
vision impairment eventually blindness in most of the working-age population. The process …