☆ 4.4 Article

Adaptive learning Unet-based adversarial network with CNN and transformer for segmentation of hard exudates in diabetes retinopathy

IET IMAGE PROCESSING (2023)

Related references

Note: Only part of the references are listed.

Article Computer Science, Interdisciplinary Applications

RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-Lesion Segmentation

Shiqi Huang et al.

Summary: Automatic diabetic retinopathy (DR) lesions segmentation is important for assisting ophthalmologists in diagnosis. We propose a relation transformer block (RTB) to handle the interactions between lesion features and vessel features, and a global transformer block (GTB) to capture small lesion patterns. Experimental results show the competitive performance of our approach compared to state-of-the-arts.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2022)

Add to Collection

Article Computer Science, Information Systems

Dual-Branch Network With Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation in Color Fundus Images

Qing Liu et al.

Summary: This paper proposes a dual-branch network with dual-sampling modulated Dice loss for automated segmentation of hard exudates in colour fundus images. The method tackles the challenges of extreme class imbalance and enormous size variation by utilizing two branches and different sampling strategies.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Progressive Multiscale Consistent Network for Multiclass Fundus Lesion Segmentation

Along He et al.

Summary: This paper proposes a progressive multi-scale consistent network (PMCNet) to address two issues in the multi-class segmentation of fundus lesions: the lack of interaction between adjacent feature levels and the conflict between low-level and high-level features. The progressive feature fusion (PFF) block is used to integrate multi-scale features, while the dynamic attention block (DAB) is used to ensure feature consistency. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2022)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Transformer Based Generative Adversarial Network for Liver Segmentation

Ugur Demir et al.

Summary: In this study, a hybrid approach combining Transformer and Generative Adversarial Network (GAN) is proposed for liver segmentation. The self-attention mechanism of Transformers improves segmentation performance, while encoding the generator into the GAN architecture allows for extracting reliable high-dimensional topology information for biomedical image segmentation.

IMAGE ANALYSIS AND PROCESSING, ICIAP 2022 WORKSHOPS, PT II (2022)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

UNETR: Transformers for 3D Medical Image Segmentation

Ali Hatamizadeh et al.

Summary: Fully Convolutional Neural Networks (FCNNs) have been successful in medical image segmentation, but their limited ability to learn long-range dependencies is a challenge. Inspired by transformers in NLP, we propose a novel architecture called UNet Transformers (UNETR) to redefine volumetric medical image segmentation as a sequence prediction problem. By combining transformers and U-shaped network design in the encoder and decoder, we effectively capture global information and achieve semantic segmentation output.

2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022) (2022)

Add to Collection

Article Biochemical Research Methods

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Fabian Isensee et al.

Summary: nnU-Net is a deep learning-based image segmentation method that automatically configures itself for diverse biological and medical image segmentation tasks, offering state-of-the-art performance as an out-of-the-box tool.

NATURE METHODS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning

Xiangde Luo et al.

Summary: Segmentation of organs or lesions from medical images is crucial in clinical applications, and traditional methods as well as CNN have limitations. A novel deep learning-based interactive segmentation method is proposed to efficiently handle complex cases, reduce user interactions, and exhibit strong generalization capabilities.

MEDICAL IMAGE ANALYSIS (2021)

Add to Collection

Article Engineering, Biomedical

Fast and efficient retinal blood vessel segmentation method based on deep learning network*

Henda Boudegga et al.

Summary: This study introduces a new Deep Learning based method for retinal vessel tree segmentation. The main contributions include a new U-form DL architecture using lightweight convolution blocks to maintain high segmentation performance while reducing computational complexity. Additionally, preprocessing and data augmentation steps are proposed to improve the accuracy of detection while reducing execution times.

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2021)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Medical Transformer: Gated Axial-Attention for Medical Image Segmentation

Jeya Maria Jose Valanarasu et al.

Summary: Deep convolutional neural networks have been widely adopted in medical image segmentation, but lack understanding of long-range dependencies due to inherent biases in convolutional architectures. Transformer-based architectures leverage self-attention mechanism to encode long-range dependencies, motivating the exploration of transformer solutions for medical image segmentation tasks.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT I (2021)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Duo-SegNet: Adversarial Dual-Views for Semi-supervised Medical Image Segmentation

Himashi Peiris et al.

Summary: Image segmentation in medical AI has been a challenge due to the need for a large amount of annotated data. To address this issue, a semi-supervised technique based on multi-view learning was proposed, outperforming state-of-the-art algorithms on multiple datasets through adversarial dual-view training.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT II (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Bin loss for hard exudates segmentation in fundus images

Song Guo et al.

NEUROCOMPUTING (2020)

Add to Collection

Article Ophthalmology

Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning

Xiangji Pan et al.

GRAEFES ARCHIVE FOR CLINICAL AND EXPERIMENTAL OPHTHALMOLOGY (2020)

Add to Collection

Article Ophthalmology

AOS THESIS 2020 Quantitative Assessment of the Severity of Diabetic Retinopathy

Srinivas R. Sadda et al.

AMERICAN JOURNAL OF OPHTHALMOLOGY (2020)

Add to Collection

Article Engineering, Biomedical

Modified U-Net architecture for semantic segmentation of diabetic retinopathy images

Nitigya Sambyal et al.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2020)

Add to Collection

Article Computer Science, Interdisciplinary Applications

UNet plus plus : Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

Zongwei Zhou et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Skin lesion segmentation via generative adversarial networks with dual discriminators

Baiying Lei et al.

MEDICAL IMAGE ANALYSIS (2020)

Add to Collection

Article Computer Science, Information Systems

Coarse-to-Fine Adversarial Networks and Zone-Based Uncertainty Analysis for NK/T-Cell Lymphoma Segmentation in CT/PET Images

Xiaobin Hu et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2020)

Add to Collection

Article Multidisciplinary Sciences

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

Zhao Shi et al.

NATURE COMMUNICATIONS (2020)

Add to Collection

Article Computer Science, Information Systems

U-net Based Method for Automatic Hard Exudates Segmentation in Fundus Images Using Inception Module and Residual Connection

Yongshuo Zong et al.

IEEE ACCESS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Towards cross-modal organ translation and segmentation: A cycle and shape-consistent generative adversarial network

Jinzheng Cai et al.

MEDICAL IMAGE ANALYSIS (2019)

Add to Collection

Article Computer Science, Information Systems

Knowledge-Aided Convolutional Neural Network for Small Organ Segmentation

Yu Zhao et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2019)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Improving Lesion Segmentation for Diabetic Retinopathy Using Adversarial Learning

Qiqi Xiao et al.

IMAGE ANALYSIS AND RECOGNITION (ICIAR 2019), PT II (2019)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

A Lightweight Neural Network for Hard Exudate Segmentation of Fundus Image

Song Guo et al.

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: IMAGE PROCESSING, PT III (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks

Juan Mo et al.

NEUROCOMPUTING (2018)

Add to Collection

Proceedings Paper Optics

Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks

Yuankai Huo et al.

MEDICAL IMAGING 2018: IMAGE PROCESSING (2018)

Add to Collection

Article Computer Science, Information Systems

Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

Prasanna Porwal et al.

DATA (2018)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Image-to-Image Translation with Conditional Adversarial Networks

Phillip Isola et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Add to Collection

© Peeref 2019-2024. All rights reserved.