527 research outputs found
Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks
We propose an automatic diabetic retinopathy (DR) analysis algorithm based on
two-stages deep convolutional neural networks (DCNN). Compared to existing
DCNN-based DR detection methods, the proposed algorithm have the following
advantages: (1) Our method can point out the location and type of lesions in
the fundus images, as well as giving the severity grades of DR. Moreover, since
retina lesions and DR severity appear with different scales in fundus images,
the integration of both local and global networks learn more complete and
specific features for DR analysis. (2) By introducing imbalanced weighting map,
more attentions will be given to lesion patches for DR grading, which
significantly improve the performance of the proposed algorithm. In this study,
we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus
images from Kaggle competition dataset. Under the guidance of clinical
ophthalmologists, the experimental results show that our local lesion detection
net achieve comparable performance with trained human observers, and the
proposed imbalanced weighted scheme also be proved to significantly improve the
capability of our DCNN-based DR grading algorithm
Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.
Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions
DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images
Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great
importance in reducing the risks of vision loss and even blindness. Ultra-wide
optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe
imaging modality in DR diagnosis system, but there is a lack of publicly
available benchmarks for model development and evaluation. To promote further
research and scientific benchmarking for diabetic retinopathy analysis using
UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy
Analysis Challenge" in conjunction with the 25th International Conference on
Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). The
challenge consists of three tasks: segmentation of DR lesions, image quality
assessment and DR grading. The scientific community responded positively to the
challenge, with 11, 12, and 13 teams from geographically diverse institutes
submitting different solutions in these three tasks, respectively. This paper
presents a summary and analysis of the top-performing solutions and results for
each task of the challenge. The obtained results from top algorithms indicate
the importance of data augmentation, model architecture and ensemble of
networks in improving the performance of deep learning models. These findings
have the potential to enable new developments in diabetic retinopathy analysis.
The challenge remains open for post-challenge registrations and submissions for
benchmarking future methodology developments
Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment
People with diabetes are more likely to develop diabetic retinopathy (DR)
than healthy people. However, DR is the leading cause of blindness. At present,
the diagnosis of diabetic retinopathy mainly relies on the experienced
clinician to recognize the fine features in color fundus images. This is a
time-consuming task. Therefore, in this paper, to promote the development of
UW-OCTA DR automatic detection, we propose a novel semi-supervised semantic
segmentation method for UW-OCTA DR image grade assessment. This method, first,
uses the MAE algorithm to perform semi-supervised pre-training on the UW-OCTA
DR grade assessment dataset to mine the supervised information in the UW-OCTA
images, thereby alleviating the need for labeled data. Secondly, to more fully
mine the lesion features of each region in the UW-OCTA image, this paper
constructs a cross-algorithm ensemble DR tissue segmentation algorithm by
deploying three algorithms with different visual feature processing strategies.
The algorithm contains three sub-algorithms, namely pre-trained MAE, ConvNeXt,
and SegFormer. Based on the initials of these three sub-algorithms, the
algorithm can be named MCS-DRNet. Finally, we use the MCS-DRNet algorithm as an
inspector to check and revise the results of the preliminary evaluation of the
DR grade evaluation algorithm. The experimental results show that the mean dice
similarity coefficient of MCS-DRNet v1 and v2 are 0.5161 and 0.5544,
respectively. The quadratic weighted kappa of the DR grading evaluation is
0.7559. Our code will be released soon
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