23 citations · 61 across the 7 of their papers we have counts for
8 papers
AI-based, automated chamber volumetry from gated, non-contrast CT
Athira J Jacob, Ola Abdelkarim, Salma Zook +11
Background: Accurate chamber volumetry from gated, non-contrast cardiac CT (NCCT) scans can be useful for potential screening of heart failure. Objectives: To validate a new, fully…
Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms
Florin Condrea, Saikiran Rapaka, Lucian Itu +4
Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death. While medical imaging, through computed tomographic pulmonary angiography (CTPA), represents the gold st…
Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery
Seung Su Yoon, Elisabeth Preuhs, Michaela Schmidt +7
Static cardiac imaging such as late gadolinium enhancement, mapping, or 3-D coronary angiography require prior information, e.g., the phase during a cardiac cycle with least motion…
Assertion Detection in Multi-Label Clinical Text using Scope Localization
Rajeev Bhatt Ambati, Ahmed Ada Hanifi, Ramya Vunikili +2
Multi-label sentences (text) in the clinical domain result from the rich description of scenarios during patient care. The state-of-theart methods for assertion detection mostly ad…
A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images
Pradeeban Kathiravelu, Puneet Sharma, Ashish Sharma +7
Executing machine learning (ML) pipelines in real-time on radiology images is hard due to the limited computing resources in clinical environments and the lack of efficient data tr…
Deep Neural Networks for ECG-free Cardiac Phase and End-Diastolic Frame Detection on Coronary Angiographies
Costin Ciusdel, Alexandru Turcea, Andrei Puiu +10
Invasive coronary angiography (ICA) is the gold standard in Coronary Artery Disease (CAD) imaging. Detection of the end-diastolic frame (EDF) and, in general, cardiac phase detecti…
Select, Attend, and Transfer: Light, Learnable Skip Connections
Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma +8
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risk…
Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
Saeid Asgari Taghanaki, Yefeng Zheng, S. Kevin Zhou +5
Simultaneous segmentation of multiple organs from different medical imaging modalities is a crucial task as it can be utilized for computer-aided diagnosis, computer-assisted surge…