activity
20242026
most citedFlow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Rubric Rewards from Item Response Theory

Milad Yazdani, Yaser Souri, Xiren Zhou +4

Many language tasks have no single answer that can be checked automatically. Rubrics provide criteria for judging responses to these tasks. For reinforcement learning, the resultin…

cs.CV2026

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…

physics.med-ph2025

AI-Augmented Thyroid Scintigraphy for Robust Classification of Disease

Maziar Sabouri, Ghasem Hajianfar, Alireza Rafiei Sardouei +11

Thyroid scintigraphy is vital for diagnosing thyroid disorders, yet deep learning (DL) models in this domain often struggle with limited, imbalanced datasets. This study investigat…

cs.CV2025★ 1 cited

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…

eess.IV2024★ 1 cited

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…