Search Results for author:
Found 7 papers, 2 papers with code
Multi-Contrast MRI Segmentation Trained on Synthetic Images
Based on synthetic image training, our segmentation results were as high as 93. 91\%, 94. 11\%, 91. 63\%, 95. 33\%, for muscle, fat, bone, and bone marrow delineation, respectively.
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images
Early detection of precancerous cysts or neoplasms, i. e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome.
Hierarchical 3D Feature Learning for Pancreas Segmentation
We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans.
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels.
Capsules for Biomedical Image Segmentation
The proposed convolutional-deconvolutional capsule network, SegCaps, shows state-of-the-art results while using a fraction of the parameters of popular segmentation networks.
Deep Learning for Musculoskeletal Image Analysis
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists.
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation
Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft tissue contrast and lack of ionizing radiation.
