Learnable ophthalmology sam
Segmentation is vital for ophthalmology image analysis. But its various modal images hinder
most of the existing segmentation algorithms applications, as they rely on training based on …
most of the existing segmentation algorithms applications, as they rely on training based on …
Rethinking Dual-Stream Super-Resolution Semantic Learning in Medical Image Segmentation
Image segmentation is fundamental task for medical image analysis, whose accuracy is
improved by the development of neural networks. However, the existing algorithms that …
improved by the development of neural networks. However, the existing algorithms that …
StructuredMesh: 3D Structured Optimization of Fa\c {c} ade Components on Photogrammetric Mesh Models using Binary Integer Programming
The lack of fa\c {c} ade structures in photogrammetric mesh models renders them
inadequate for meeting the demands of intricate applications. Moreover, these mesh models …
inadequate for meeting the demands of intricate applications. Moreover, these mesh models …
Optimized Hard Exudate Detection with Supervised Contrastive Learning
Diabetic retinopathy (DR) is a leading global cause of blindness. Early detection of hard
exudates plays a crucial role in identifying DR, which aids in treating diabetes and …
exudates plays a crucial role in identifying DR, which aids in treating diabetes and …
Segment anything model-based crack segmentation using low-rank adaption fine-tuning
Y Guo, Y Xu, H Cui, M Dang… - Structural Health …, 2024 - journals.sagepub.com
High-precision crack segmentation is crucial for analyzing and maintaining the apparent
state of structures. The introduction of large vision models, such as the segment anything …
state of structures. The introduction of large vision models, such as the segment anything …
Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model
H Wang, Y Chen, W Chen, H Xu, H Zhao… - arXiv preprint arXiv …, 2024 - arxiv.org
Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images capture high-
resolution views of the retina with typically 200 spanning degrees. Accurate segmentation of …
resolution views of the retina with typically 200 spanning degrees. Accurate segmentation of …
Efficient multi-scale learning via scale embedding for diabetic retinopathy multi-lesion segmentation
S Guo - Biomedical Signal Processing and Control, 2025 - Elsevier
Automated segmentation of diabetic retinopathy (DR) lesions in fundus images is significant
in computer-aided diagnosis. While numerous studies have tackled lesion segmentation …
in computer-aided diagnosis. While numerous studies have tackled lesion segmentation …
NetraDeep: An Integrated Deep Learning and Image Processing System for Precise Detection of Hard Exudates
Hard exudate (HE) is a common manifestation of various eye diseases, such as diabetic
retinopathy (DR), and a prominent cause of vision loss and blindness. Researchers aim to …
retinopathy (DR), and a prominent cause of vision loss and blindness. Researchers aim to …
[PDF][PDF] WSRFNet: wavelet-based scale-specific recurrent feedback network for diabetic retinopathy lesion segmentation
X Li, X Wu - Proceedings of the Thirty-Third International Joint …, 2024 - ijcai.org
Diabetic retinopathy lesion segmentation (DRLS) faces a challenge of significant variation in
the size of different lesions. An effective method to address this challenge is to fuse multi …
the size of different lesions. An effective method to address this challenge is to fuse multi …
A global and patch-wise contrastive loss for accurate automated exudate detection
Diabetic retinopathy (DR) is a leading global cause of blindness. Early detection of hard
exudates plays a crucial role in identifying DR, which aids in treating diabetes and …
exudates plays a crucial role in identifying DR, which aids in treating diabetes and …