Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …
imaging. However, these approaches primarily focus on supervised learning, assuming that …
Narrowing the semantic gaps in u-net with learnable skip connections: The case of medical image segmentation
Current state-of-the-art medical image segmentation techniques predominantly employ the
encoder–decoder architecture. Despite its widespread use, this U-shaped framework …
encoder–decoder architecture. Despite its widespread use, this U-shaped framework …
Dealmvc: Dual contrastive calibration for multi-view clustering
Benefiting from the strong view-consistent information mining capacity, multi-view
contrastive clustering has attracted plenty of attention in recent years. However, we observe …
contrastive clustering has attracted plenty of attention in recent years. However, we observe …
Auto-metric graph neural network optimized with capuchin search optimization algorithm for coinciding diabetic retinopathy and diabetic macular edema grading
Diabetic retinopathy (DR) and diabetic macular edema (DME) are the major eternal
blindness in aged people. In this manuscript, Auto-Metric Graph Neural Network (AGNN) …
blindness in aged people. In this manuscript, Auto-Metric Graph Neural Network (AGNN) …
A comprehensive review of artificial intelligence models for screening major retinal diseases
This paper provides a systematic survey of artificial intelligence (AI) models that have been
proposed over the past decade to screen retinal diseases, which can cause severe visual …
proposed over the past decade to screen retinal diseases, which can cause severe visual …
CRA-Net: Transformer guided category-relation attention network for diabetic retinopathy grading
F Zang, H Ma - Computers in Biology and Medicine, 2024 - Elsevier
Automated grading of diabetic retinopathy (DR) is an important means for assisting clinical
diagnosis and preventing further retinal damage. However, imbalances and similarities …
diagnosis and preventing further retinal damage. However, imbalances and similarities …
Grading the severity of diabetic retinopathy using an ensemble of self-supervised pre-trained convolutional neural networks: ESSP-CNNs
S Parsa, T Khatibi - Multimedia Tools and Applications, 2024 - Springer
Diabetic retinopathy (DR) is a common eye disorder that can lead to vision problems and
blindness, necessitating accurate grading for effective treatment. While various artificial …
blindness, necessitating accurate grading for effective treatment. While various artificial …
Lesion-Aware Contrastive Learning for Diabetic Retinopathy Diagnosis
Early diagnosis and screening of diabetic retinopathy are critical in reducing the risk of
vision loss in patients. However, in a real clinical situation, manual annotation of lesion …
vision loss in patients. However, in a real clinical situation, manual annotation of lesion …
Self-supervised domain adaptation for breaking the limits of low-quality fundus image quality enhancement
Retinal fundus images have been applied for the diagnosis and screening of eye diseases,
such as Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). However, both low …
such as Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). However, both low …
Multi-lesion segmentation guided deep attention network for automated detection of diabetic retinopathy
F Li, X Sheng, H Wei, S Tang, H Zou - Computers in Biology and Medicine, 2024 - Elsevier
Accurate multi-lesion segmentation together with automated grading on fundus images
played a vital role in diagnosing and treating diabetic retinopathy (DR). Nevertheless, the …
played a vital role in diagnosing and treating diabetic retinopathy (DR). Nevertheless, the …