58 citations · 97 across the 11 of their papers we have counts for
12 papers
Deconver: A Deconvolutional Network for Medical Image Segmentation
Pooya Ashtari, Shahryar Noei, Fateme Nateghi Haredasht +4
While convolutional neural networks (CNNs) and vision transformers (ViTs) have advanced medical image segmentation, they face inherent limitations such as local receptive fields in…
Quantization-aware Matrix Factorization for Low Bit Rate Image Compression
Pooya Ashtari, Pourya Behmandpoor, Fateme Nateghi Haredasht +3
Lossy image compression is essential for efficient transmission and storage. Traditional compression methods mainly rely on discrete cosine transform (DCT) or singular value decomp…
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew +55
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable A…
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation
Pooya Ashtari, Diana M. Sima, Lieven De Lathauwer +3
Convolutional Neural Networks (CNNs) with U-shaped architectures have dominated medical image segmentation, which is crucial for various clinical purposes. However, the inherent lo…
Automatic artifact removal of resting-state fMRI with Deep Neural Networks
Christos Theodoropoulos, Christos Chatzichristos, Sabine Van Huffel
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for studying brain activity. During an fMRI session, the subject executes a set of tasks (task-related fMRI…
Early soft and flexible fusion of EEG and fMRI via tensor decompositions
Christos Chatzichristos, Eleftherios Kofidis, Lieven De Lathauwer +2
Data fusion refers to the joint analysis of multiple datasets which provide complementary views of the same task. In this preprint, the problem of jointly analyzing electroencephal…