97 citations · 489 across the 41 of their papers we have counts for
49 papers
TRIUNE-Net: Harmonizing Scale, Shape, and Efficiency in Pancreatic Tumor Segmentation
Amir Hossein Saleknia, Alireza Kheyrkhah, Sanaz Karimijafarbigloo +5
Pancreatic tumor segmentation in 3D CT volumes is challenged by extreme scale variability across both the pancreas and tumor, and highly irregular tumor morphology. While recent ad…
Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation
Sanaz Karimijafarbigloo, Armin Khosravi, Alireza Kheyrkhah +3
Multi-rater medical image segmentation captures the inherent ambiguity of clinical interpretation, where diagnostic boundaries vary across experts and imaging devices. Existing app…
CENet: Context Enhancement Network for Medical Image Segmentation
Afshin Bozorgpour, Sina Ghorbani Kolahi, Reza Azad +2
Medical image segmentation, particularly in multi-domain scenarios, requires precise preservation of anatomical structures across diverse representations. While deep learning has a…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
SLA-INR: Single-Layer Learnable Activation for Implicit Neural Representation
Moein Heidari, Reza Rezaeian, Reza Azad +3
Implicit Neural Representation (INR), leveraging a neural network to transform coordinate input into corresponding attributes, has recently driven significant advances in several v…
MSANet: Multi-scale Adaptive Attention-guided Network for Medical Image Segmentation
Sina Ghorbani Kolahi, Seyed Kamal Chaharsooghi, Toktam Khatibi +5
Medical image segmentation involves identifying and separating object instances in a medical image to delineate various tissues and structures, a task complicated by the significan…
Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis
Moein Heidari, Sina Ghorbani Kolahi, Sanaz Karimijafarbigloo +8
Sequence modeling plays a vital role across various domains, with recurrent neural networks being historically the predominant method of performing these tasks. However, the emerge…
LHU-Net: a Lean Hybrid U-Net for Cost-efficient, High-performance Volumetric Segmentation
Yousef Sadegheih, Afshin Bozorgpour, Pratibha Kumari +2
The rise of Transformer architectures has advanced medical image segmentation, leading to hybrid models that combine Convolutional Neural Networks (CNNs) and Transformers. However,…
Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights
Moein Heidari, Reza Azad, Sina Ghorbani Kolahi +8
Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various Comput…
Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook
Reza Azad, Moein Heidary, Kadir Yilmaz +5
Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. As the predomi…
Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects
Pratibha Kumari, Joohi Chauhan, Afshin Bozorgpour +3
Medical imaging analysis has witnessed remarkable advancements even surpassing human-level performance in recent years, driven by the rapid development of advanced deep-learning al…
FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation
Amirhossein Kazerouni, Sanaz Karimijafarbigloo, Reza Azad +3
Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training. In response to this challenge, we introduce Fu…
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning
Sanaz Karimijafarbigloo, Reza Azad, Yury Velichko +2
Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-la…
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling
Afshin Bozorgpour, Bobby Azad, Reza Azad +3
Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or…
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
Amirhossein Kazerouni, Reza Azad, Alireza Hosseini +2
Implicit Neural Representations (INRs) have revolutionized signal representation by leveraging neural networks to provide continuous and smooth representations of complex data. How…
Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision
Bobby Azad, Reza Azad, Sania Eskandari +4
Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning proble…
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…
Self-supervised Semantic Segmentation: Consistency over Transformation
Sanaz Karimijafarbigloo, Reza Azad, Amirhossein Kazerouni +3
Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures. However, prevailing supervised deep learning approaches for medical…
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…
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation
Afshin Bozorgpour, Yousef Sadegheih, Amirhossein Kazerouni +2
Skin lesion segmentation plays a critical role in the early detection and accurate diagnosis of dermatological conditions. Denoising Diffusion Probabilistic Models (DDPMs) have rec…
Implicit Neural Representation in Medical Imaging: A Comparative Survey
Amirali Molaei, Amirhossein Aminimehr, Armin Tavakoli +4
Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing n…
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach
Sanaz Karimijafarbigloo, Reza Azad, Dorit Merhof
Few-shot semantic segmentation (FSS) offers immense potential in the field of medical image analysis, enabling accurate object segmentation with limited training data. However, exi…
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…