97 citations · 215 across the 12 of their papers we have counts for
9 papers · 1 filter
VAMOS-OCTA: Vessel-Aware Multi-Axis Orthogonal Supervision for Inpainting Motion-Corrupted OCT Angiography Volumes
Nick DiSanto, Ehsan Khodapanah Aghdam, Han Liu +4
Handheld Optical Coherence Tomography Angiography (OCTA) enables noninvasive retinal imaging in uncooperative or pediatric subjects, but is highly susceptible to motion artifacts t…
Monocular absolute depth estimation from endoscopy via domain-invariant feature learning and latent consistency
Hao Li, Daiwei Lu, Jesse d'Almeida +8
Monocular depth estimation (MDE) is a critical task to guide autonomous medical robots. However, obtaining absolute (metric) depth from an endoscopy camera in surgical scenes is di…
Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
Reza Azad, Amirhossein Kazerouni, Alaa Sulaiman +4
Medical image segmentation is a critical task that plays a vital role in diagnosis, treatment planning, and disease monitoring. Accurate segmentation of anatomical structures and a…
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation
Reza Azad, Leon Niggemeier, Michael Huttemann +5
Medical image segmentation has seen significant improvements with transformer models, which excel in grasping far-reaching contexts and global contextual information. However, the…
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection
Reza Azad, Amirhossein Kazerouni, Babak Azad +4
Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks. However, compared to the Convolutional Neural Network (CNN) models, it ha…
Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach
Reza Azad, Yiwei Jia, Ehsan Khodapanah Aghdam +2
While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suffer from limitations in capturing long-ra…
Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review
Reza Azad, Amirhossein Kazerouni, Moein Heidari +6
The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transfo…
DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation
Reza Azad, René Arimond, Ehsan Khodapanah Aghdam +2
Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies. However, the self-attention mechanism, which is the…
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
Moein Heidari, Amirhossein Kazerouni, Milad Soltany +4
Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation in modeling long-range dependencies and…