2 citations · 4 across the 7 of their papers we have counts for
9 papers
Every Pixel Is an Option: A System One Model for Medical Ultrasound Biometry
Moein Heidari, Junbo Rao, Yudan Chen +6
**Purpose:** Ultrasound biometry is widely used across various clinical applications, including prenatal and cardiac examinations, to obtain quantitative measurements by placing ca…
VAMPS: Visual-Assisted Mathematical Problem Solving Benchmark
Amirhossein Dabiriaghdam, Shayan Vassef, Mohammadreza Bakhtiari +5
Multimodal large language models are increasingly capable of complex reasoning, yet their performance often degrades when they must externalize a problem through a tool and then re…
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…
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…
Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality
Milad Yazdani, Yasamin Medghalchi, Pooria Ashrafian +2
Deep learning models have emerged as a powerful tool for various medical applications. However, their success depends on large, high-quality datasets that are challenging to obtain…
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-…
MEDDAP: Medical Dataset Enhancement via Diversified Augmentation Pipeline
Yasamin Medghalchi, Niloufar Zakariaei, Arman Rahmim +1
The effectiveness of Deep Neural Networks (DNNs) heavily relies on the abundance and accuracy of available training data. However, collecting and annotating data on a large scale i…
Benchmarking Robustness to Text-Guided Corruptions
Mohammadreza Mofayezi, Yasamin Medghalchi
This study investigates the robustness of image classifiers to text-guided corruptions. We utilize diffusion models to edit images to different domains. Unlike other works that use…
A generic diffusion-based approach for 3D human pose prediction in the wild
Saeed Saadatnejad, Ali Rasekh, Mohammadreza Mofayezi +4
Predicting 3D human poses in real-world scenarios, also known as human pose forecasting, is inevitably subject to noisy inputs arising from inaccurate 3D pose estimations and occlu…