19 citations · 53 across the 6 of their papers we have counts for
5 papers · 1 filter
Multi-Contrast MRI Segmentation Trained on Synthetic Images
Ismail Irmakci, Zeki Emre Unel, Nazli Ikizler-Cinbis +1
In our comprehensive experiments and evaluations, we show that it is possible to generate multiple contrast (even all synthetically) and use synthetically generated images to train…
Hierarchical 3D Feature Learning for Pancreas Segmentation
Federica Proietto Salanitri, Giovanni Bellitto, Ismail Irmakci +3
We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans. More specifically, the proposed model consists of a 3D encode…
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
Ugur Demir, Ismail Irmakci, Elif Keles +7
Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradi…
Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI
Syed Muhammad Anwar, Ismail Irmakci, Drew A. Torigian +5
Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and rese…
Deep Learning for Musculoskeletal Image Analysis
Ismail Irmakci, Syed Muhammad Anwar, Drew A. Torigian +1
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and…