8 citations · 15 across the 3 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…
Self-Supervised Out-of-Distribution Detection in Brain CT Scans
Abinav Ravi Venkatakrishnan, Seong Tae Kim, Rami Eisawy +2
Medical imaging data suffers from the limited availability of annotation because annotating 3D medical data is a time-consuming and expensive task. Moreover, even if the annotation…
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…