48 citations · 77 across the 18 of their papers we have counts for
24 papers
Which Pretext Task Transfers? Self-Supervised Pretraining Objectives for Lung Ultrasound
Moein Heidari, Junbo Rao, Jai Choraria +3
Self-supervised learning (SSL) can reduce the need for labelled medical images, but the choice of pretext objective remains unclear for lung ultrasound (LUS). Contrastive learning,…
MaLViL: Multi-axis Low-rank Vision-LSTM for Medical Image Segmentation
Afshin Bozorgpour, Sina Ghorbani Kolahi, Moein Heidari +2
Vision-LSTM (ViL) enables efficient global modeling, but its cost still scales with the number of spatial tokens, so existing segmenters confine ViL to a coarse bottleneck and lose…
XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity
Alvin Kimbowa, Moein Heidari, David Liu +1
While U-Net architectures remain the gold standard for medical image segmentation, their deployment in resource-constrained environments demands aggressive model compression. Howev…
When Minor Edits Matter: LLM-Driven Prompt Attack for Medical VLM Robustness in Ultrasound
Yasamin Medghalchi, Milad Yazdani, Amirhossein Dabiriaghdam +7
Ultrasound is widely used in clinical practice due to its portability, cost-effectiveness, safety, and real-time imaging capabilities. However, image acquisition and interpretation…
Echo-CoPilot: A Multiple-Perspective Agentic Framework for Reliable Echocardiography Interpretation
Moein Heidari, Ali Mehrabian, Mohammad Amin Roohi +5
Echocardiography interpretation requires integrating multi-view temporal evidence with quantitative measurements and guideline-grounded reasoning, yet existing foundation-model pip…
WaRA: Wavelet Low-Rank Adaptation for Medical Image Classification
Moein Heidari, Yijin Huang, Yasamin Medghalchi +4
Adapting large pretrained vision models to medical image classification is often limited by memory, computation, and task-specific specializations. Parameter-efficient fine-tuning…
Echo-ENet: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation
Moein Heidari, Afshin Bozorgpour, AmirHossein Zarif-Fakharnia +6
Left ventricular ejection fraction (LVEF) is a primary marker of cardiac function. However, routine estimation from endocardial measurements requires manual delineation at end-dias…
A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation
Moein Heidari, Ehsan Khodapanah Aghdam, Alexander Manzella +5
The retroperitoneum hosts a variety of tumors, including rare benign and malignant types, which pose diagnostic and treatment challenges due to their infrequency and proximity to v…
Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attacks on Breast Ultrasound Images
Yasamin Medghalchi, Moein Heidari, Clayton Allard +2
Deep neural networks (DNNs) offer significant promise for improving breast cancer diagnosis in medical imaging. However, these models are highly susceptible to adversarial attacks-…
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…
Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks
Ali Mehrabian, Parsa Mojarad Adi, Moein Heidari +1
Implicit neural representations (INRs) use neural networks to provide continuous and resolution-independent representations of complex signals with a small number of parameters. Ho…
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…
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…
Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks
Pooria Ashrafian, Milad Yazdani, Moein Heidari +2
High-quality, large-scale data is essential for robust deep learning models in medical applications, particularly ultrasound image analysis. Diffusion models facilitate high-fideli…
DiffGANPaint: Fast Inpainting Using Denoising Diffusion GANs
Moein Heidari, Alireza Morsali, Tohid Abedini +1
Free-form image inpainting is the task of reconstructing parts of an image specified by an arbitrary binary mask. In this task, it is typically desired to generalize model capabili…
SA2-Net: Scale-aware Attention Network for Microscopic Image Segmentation
Mustansar Fiaz, Moein Heidari, Rao Muhammad Anwer +1
Microscopic image segmentation is a challenging task, wherein the objective is to assign semantic labels to each pixel in a given microscopic image. While convolutional neural netw…
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…
Diffusion Models for Medical Image Analysis: A Comprehensive Survey
Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari +4
Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward…
TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation
Reza Azad, Moein Heidari, Moein Shariatnia +4
Convolutional neural networks (CNNs) have been the de facto standard in a diverse set of computer vision tasks for many years. Especially, deep neural networks based on seminal arc…
TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model
Reza Azad, Mohammad T. AL-Antary, Moein Heidari +1
In the past few years, convolutional neural networks (CNNs), particularly U-Net, have been the prevailing technique in the medical image processing era. Specifically, the seminal U…
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…
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Reza Azad, Moein Heidari, Julien Cohen-Adad +2
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related diseases such as osteoporosis, vertebral frac…
Contextual Attention Network: Transformer Meets U-Net
Reza Azad, Moein Heidari, Yuli Wu +1
Currently, convolutional neural networks (CNN) (e.g., U-Net) have become the de facto standard and attained immense success in medical image segmentation. However, as a downside, C…