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Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration
Monika Grewal, Jan Wiersma, Henrike Westerveld +2
Purpose: Deformable Image Registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudie…
An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images
Monika Grewal, Timo M. Deist, Jan Wiersma +2
Anatomical landmark correspondences in medical images can provide additional guidance information for the alignment of two images, which, in turn, is crucial for many medical appli…
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Pulkit Kumar, Monika Grewal, Muktabh Mayank Srivastava
Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases. With the availability of ChestX-ray14, which is a massive dataset of chest X…
Anatomical labeling of brain CT scan anomalies using multi-context nearest neighbor relation networks
Srikrishna Varadarajan, Muktabh Mayank Srivastava, Monika Grewal +1
This work is an endeavor to develop a deep learning methodology for automated anatomical labeling of a given region of interest (ROI) in brain computed tomography (CT) scans. We co…
RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans
Monika Grewal, Muktabh Mayank Srivastava, Pulkit Kumar +1
We describe a deep learning approach for automated brain hemorrhage detection from computed tomography (CT) scans. Our model emulates the procedure followed by radiologists to anal…