25 citations · 35 across the 9 of their papers we have counts for
9 papers
Less Contouring, More Accuracy: Lesion-Guided ROI Deep Learning for Ovarian Ultrasound Classification
Mehran Ahmad, Ali Abbasian Ardakani, Afshin Mohammadi +3
Ovarian lesion classification using transvaginal ultrasound remains challenging due to overlapping imaging characteristics and the dependence on expert interpretation. This study i…
Multimodal Connectome Fusion via Cross-Attention for Autism Spectrum Disorder Classification Using Graph Learning
Ansar Rahman, Hassan Shojaee-Mend, Sepideh Hatamikia
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by atypical functional brain connectivity and subtle structural alterations. rs-fMRI has been…
NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation
Nima Torbati, Anastasia Meshcheryakova, Ramona Woitek +3
Nuclei instance segmentation in hematoxylin and eosin (H&E)-stained images plays an important role in automated histological image analysis, with various applications in downstream…
Developing Predictive and Robust Radiomics Models for Chemotherapy Response in High-Grade Serous Ovarian Carcinoma
Sepideh Hatamikia, Geevarghese George, Florian Schwarzhans +10
Objectives: High-grade serous ovarian carcinoma (HGSOC) is typically diagnosed at an advanced stage with extensive peritoneal metastases, making treatment challenging. Neoadjuvant…
From ACR O-RADS 2022 to Explainable Deep Learning: Comparative Performance of Expert Radiologists, Convolutional Neural Networks, Vision Transformers, and Fusion Models in Ovarian Masses
Ali Abbasian Ardakani, Afshin Mohammadi, Alisa Mohebbi +6
Background: The 2022 update of the Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound classification refines risk stratification for adnexal lesions, yet human interpret…
Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image Retrieval
Amirreza Mahbod, Nematollah Saeidi, Sepideh Hatamikia +1
Medical image retrieval refers to the task of finding similar images for given query images in a database, with applications such as diagnosis support. While traditional medical im…
Intensity Normalization Techniques and Their Effect on the Robustness and Predictive Power of Breast MRI Radiomics
Florian Schwarzhans, Geevarghese George, Lorena Escudero Sanchez +4
Radiomics analysis has emerged as a promising approach for extracting quantitative features from medical images to aid in cancer diagnosis and treatment. However, radiomics researc…
Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images
Nematollah Saeidi, Hossein Karshenas, Bijan Shoushtarian +3
Breast cancer is the most common cancer type in women worldwide. Early detection and appropriate treatment can significantly reduce its impact. While histopathology examinations pl…
Breast MRI radiomics and machine learning radiomics-based predictions of response to neoadjuvant chemotherapy -- how are they affected by variations in tumour delineation?
Sepideh Hatamikia, Geevarghese George, Florian Schwarzhans +2
Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variabi…