Going deep in medical image analysis: concepts, methods, challenges, and future directions
Medical image analysis is currently experiencing a paradigm shift due to deep learning. This
technology has recently attracted so much interest of the Medical Imaging Community that it …
technology has recently attracted so much interest of the Medical Imaging Community that it …
Retinal vessel segmentation using deep learning: a review
This paper presents a comprehensive review of retinal blood vessel segmentation based on
deep learning. The geometric characteristics of retinal vessels reflect the health status of …
deep learning. The geometric characteristics of retinal vessels reflect the health status of …
DUNet: A deformable network for retinal vessel segmentation
Automatic segmentation of retinal vessels in fundus images plays an important role in the
diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose …
diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose …
DENSE-INception U-net for medical image segmentation
Background and objective Convolutional neural networks (CNNs) play an important role in
the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture …
the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture …
Dive into the details of self-supervised learning for medical image analysis
Self-supervised learning (SSL) has achieved remarkable performance in various medical
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific …
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific …
Lightweight attention convolutional neural network for retinal vessel image segmentation
Retinal vessel image is an important biological information that can be used for personal
identification in the social security domain, and for disease diagnosis in the medical domain …
identification in the social security domain, and for disease diagnosis in the medical domain …
Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation
Most recent research of deep neural networks in the field of computer vision has focused on
improving performances of point predictions by developing network architectures or learning …
improving performances of point predictions by developing network architectures or learning …
Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification
Multi-task learning aims to improve generalization performance of multiple prediction tasks
by appropriately sharing relevant information across them. In the context of deep neural …
by appropriately sharing relevant information across them. In the context of deep neural …
Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function
K Hu, Z Zhang, X Niu, Y Zhang, C Cao, F Xiao, X Gao - Neurocomputing, 2018 - Elsevier
Retinal vessel analysis of fundus images is an indispensable method for the screening and
diagnosis of related diseases. In this paper, we propose a novel retinal vessel segmentation …
diagnosis of related diseases. In this paper, we propose a novel retinal vessel segmentation …
A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images
JI Orlando, E Prokofyeva… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
Goal: In this work, we present an extensive description and evaluation of our method for
blood vessel segmentation in fundus images based on a discriminatively trained fully …
blood vessel segmentation in fundus images based on a discriminatively trained fully …