48 citations · 169 across the 49 of their papers we have counts for
53 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…
Analyzing Image Encoder Choices and Graph Homophily in GCN Frameworks for Breast Ultrasound Classification
Sabahattin Mert Daloglu, Ceren Coskun, Harvey Castro +2
Breast ultrasound is widely used for screening, yet automated analysis remains challenging due to speckle noise, acquisition variability, and weak separation of benign and malignan…
Learning from Complementary Ultrasound Representations for Liver Disease Classification
Sabahattin Mert Daloglu, Gokce Bekar, Ceren Coskun +5
Differentiating non-alcoholic steatohepatitis (NASH) from non-alcoholic fatty liver disease (NAFLD) using ultrasound remains challenging due to subtle tissue alterations and the li…
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…
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…
MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices
Alvin Kimbowa, Arjun Parmar, Ibrahim Mujtaba +5
Objective: To develop a robust and compact deep learning model for automated knee cartilage segmentation on point-of-care ultrasound (POCUS) devices. Methods: We propose MonoUNet,…
A Clinically Anchored Radiomics Dictionary for Explainable TI-RADS-Based Thyroid Nodule Classification in Ultrasound; Dictionary Version TU1.0
Mohammad Salmanpour, Shahram Taeb, Ali Fathi Jouzdani +5
Artificial intelligence based radiomics models for thyroid ultrasound (US) often achieve strong diagnostic performance but remain difficult to interpret, limiting clinical trust an…
Representation-Level Adversarial Regularization for Clinically Aligned Multitask Thyroid Ultrasound Assessment
Dina Salama, Mohamed Mahmoud, Nourhan Bayasi +2
Thyroid ultrasound is the first-line exam for assessing thyroid nodules and determining whether biopsy is warranted. In routine reporting, radiologists produce two coupled outputs:…
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…
Enhancement Without Contrast: Stability-Aware Multicenter Machine Learning for Glioma MRI Imaging
Sajad Amiri, Shahram Taeb, Sara Gharibi +6
Gadolinium-based contrast agents (GBCAs) are central to glioma imaging but raise safety, cost, and accessibility concerns. Predicting contrast enhancement from non-contrast MRI usi…
Handcrafted vs. Deep Radiomics vs. Fusion vs. Deep Learning: A Comprehensive Review of Machine Learning -Based Cancer Outcome Prediction in PET and SPECT Imaging
Mohammad R. Salmanpour, Somayeh Sadat Mehrnia, Sajad Jabarzadeh Ghandilu +7
Machine learning (ML), including deep learning (DL) and radiomics-based methods, is increasingly used for cancer outcome prediction with PET and SPECT imaging. However, the compara…
Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
Mohammad R. Salmanpour, Amir Hossein Pouria, Sonia Falahati +7
Background: CT imaging is vital for lung cancer management, offering detailed visualization for AI-based prognosis. However, supervised learning SL models require large labeled dat…
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…
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…
AllMetrics: A Unified Python Library for Standardized Metric Evaluation and Robust Data Validation in Machine Learning
Morteza Alizadeh, Mehrdad Oveisi, Sonya Falahati +6
Machine learning (ML) models rely heavily on consistent and accurate performance metrics to evaluate and compare their effectiveness. However, existing libraries often suffer from…
Pathobiological Dictionary Defining Pathomics and Texture Features: Addressing Understandable AI Issues in Personalized Liver Cancer; Dictionary Version LCP1.0
Mohammad R. Salmanpour, Seyed Mohammad Piri, Somayeh Sadat Mehrnia +5
Artificial intelligence (AI) holds strong potential for medical diagnostics, yet its clinical adoption is limited by a lack of interpretability and generalizability. This study int…
Mono2D: A Trainable Monogenic Layer for Robust Knee Cartilage Segmentation on Out-of-Distribution 2D Ultrasound Data
Alvin Kimbowa, Arjun Parmar, Maziar Badii +3
Automated knee cartilage segmentation using point-of-care ultrasound devices and deep-learning networks has the potential to enhance the management of knee osteoarthritis. However,…
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…
Dataset Properties Shape the Success of Neuroimaging-Based Patient Stratification: A Benchmarking Analysis Across Clustering Algorithms
Yuetong Yu, Ruiyang Ge, Ilker Hacihaliloglu +3
Background: Data driven stratification of patients into biologically informed subtypes holds promise for precision neuropsychiatry, yet neuroimaging-based clustering methods often…
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…
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…
Influence of High-Performance Image-to-Image Translation Networks on Clinical Visual Assessment and Outcome Prediction: Utilizing Ultrasound to MRI Translation in Prostate Cancer
Mohammad R. Salmanpour, Amin Mousavi, Yixi Xu +2
Purpose: This study examines the core traits of image-to-image translation (I2I) networks, focusing on their effectiveness and adaptability in everyday clinical settings. Methods:…