6 papers
Mixed-Block Neural Architecture Search for Medical Image Segmentation
Martijn M. A. Bosma, Arkadiy Dushatskiy, Monika Grewal +2
Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cas…
Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization
Timo M. Deist, Monika Grewal, Frank J. W. M. Dankers +2
Real-world problems are often multi-objective with decision-makers unable to specify a priori which trade-off between the conflicting objectives is preferable. Intuitively, buildin…
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…