[HTML][HTML] Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …
grows, especially in high-stakes decision making areas such as medical image analysis …
Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning …
L Qiao, Y Zhu, H Zhou - IEEE Access, 2020 - ieeexplore.ieee.org
Predicting the presence of Microaneurysms in the fundus images and the identification of
diabetic retinopathy in early-stage has always been a major challenge for decades. Diabetic …
diabetic retinopathy in early-stage has always been a major challenge for decades. Diabetic …
A survey on medical image analysis in diabetic retinopathy
Diabetic Retinopathy (DR) represents a highly-prevalent complication of diabetes in which
individuals suffer from damage to the blood vessels in the retina. The disease manifests …
individuals suffer from damage to the blood vessels in the retina. The disease manifests …
DR| GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images
Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and
follow up of patient, but the screening process can be tiresome and prone to errors. Deep …
follow up of patient, but the screening process can be tiresome and prone to errors. Deep …
An enhanced residual U-Net for microaneurysms and exudates segmentation in fundus images
C Kou, W Li, Z Yu, L Yuan - IEEE Access, 2020 - ieeexplore.ieee.org
Diabetic retinopathy (DR) is a leading cause of visual blindness. However if DR can be
diagnosed and treated early, 90% of DR causing blindness can be prevented significantly …
diagnosed and treated early, 90% of DR causing blindness can be prevented significantly …
[PDF][PDF] Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN.
Diabetes is a metabolic disorder that results in a retinal complication called diabetic
retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe …
retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe …
Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets
Purpose: Diabetic retinopathy (DR) is characterized by retinal lesions affecting people
having diabetes for several years. It is one of the leading causes of visual impairment …
having diabetes for several years. It is one of the leading causes of visual impairment …
Transfer learning for diabetic retinopathy detection: a study of dataset combination and model performance
AM Mutawa, S Alnajdi, S Sruthi - Applied Sciences, 2023 - mdpi.com
Diabetes' serious complication, diabetic retinopathy (DR), which can potentially be life-
threatening, might result in vision loss in certain situations. Although it has no symptoms in …
threatening, might result in vision loss in certain situations. Although it has no symptoms in …
Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network
C Kou, W Li, W Liang, Z Yu… - Journal of Medical Imaging, 2019 - spiedigitallibrary.org
Microaneurysms (MAs) play an important role in the diagnosis of clinical diabetic retinopathy
at the early stage. Annotation of MAs manually by experts is laborious and so it is essential …
at the early stage. Annotation of MAs manually by experts is laborious and so it is essential …