8 citations · 8 across the 2 of their papers we have counts for
3 papers
GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images
Aadhithya Sankar, Matthias Keicher, Rami Eisawy +4
Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically gene…
Train, Learn, Expand, Repeat
Abhijeet Parida, Aadhithya Sankar, Rami Eisawy +4
High-quality labeled data is essential to successfully train supervised machine learning models. Although a large amount of unlabeled data is present in the medical domain, labelin…
Learn to Segment Organs with a Few Bounding Boxes
Abhijeet Parida, Arianne Tran, Nassir Navab +1
Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology. Deep neural networks…