1 code implementation • 5 Oct 2023 • Yingcheng Liu, Neerav Karani, Neel Dey, S. Mazdak Abulnaga, Junshen Xu, P. Ellen Grant, Esra Abaci Turk, Polina Golland
The placenta plays a crucial role in fetal development.
no code implementations • 16 Jul 2023 • Neerav Karani, Neel Dey, Polina Golland
Neural network prediction probabilities and accuracy are often only weakly-correlated.
1 code implementation • 10 Feb 2022 • Neerav Karani, Georg Brunner, Ertunc Erdil, Simin Fei, Kerem Tezcan, Krishna Chaitanya, Ender Konukoglu
We use 1D marginal distributions of a trained task CNN's features as experts in the FoE model.
no code implementations • 17 Dec 2021 • Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu
In this paper, we propose a local contrastive loss to learn good pixel level features useful for segmentation by exploiting semantic label information obtained from pseudo-labels of unlabeled images alongside limited annotated images.
1 code implementation • 30 Sep 2020 • Kerem C. Tezcan, Neerav Karani, Christian F. Baumgartner, Ender Konukoglu
In this paper, we propose a method that instead returns multiple images which are possible under the acquisition model and the chosen prior to capture the uncertainty in the inversion process.
1 code implementation • 9 Jul 2020 • Krishna Chaitanya, Neerav Karani, Christian F. Baumgartner, Ertunc Erdil, Anton Becker, Olivio Donati, Ender Konukoglu
In this work, we propose a novel task-driven data augmentation method for learning with limited labeled data where the synthetic data generator, is optimized for the segmentation task.
1 code implementation • 9 Jul 2020 • Anna Volokitin, Ertunc Erdil, Neerav Karani, Kerem Can Tezcan, Xiaoran Chen, Luc van Gool, Ender Konukoglu
We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices.
1 code implementation • NeurIPS 2020 • Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu
In this work, we propose strategies for extending the contrastive learning framework for segmentation of volumetric medical images in the semi-supervised setting with limited annotations, by leveraging domain-specific and problem-specific cues.
1 code implementation • 18 Jun 2020 • Ertunc Erdil, Krishna Chaitanya, Neerav Karani, Ender Konukoglu
The results demonstrate that the proposed method consistently achieves high OOD detection performance in both classification and segmentation tasks and improves state-of-the-art in almost all cases.
2 code implementations • 9 Apr 2020 • Neerav Karani, Ertunc Erdil, Krishna Chaitanya, Ender Konukoglu
In medical image segmentation, this premise is violated when there is a mismatch between training and test images in terms of their acquisition details, such as the scanner model or the protocol.
1 code implementation • 11 Feb 2019 • Krishna Chaitanya, Neerav Karani, Christian Baumgartner, Olivio Donati, Anton Becker, Ender Konukoglu
However, there is potential to improve the approach by (i) explicitly modeling deformation fields (non-affine spatial transformation) and intensity transformations and (ii) leveraging unlabelled data during the generative process.
1 code implementation • 25 May 2018 • Neerav Karani, Krishna Chaitanya, Christian Baumgartner, Ender Konukoglu
We evaluate the method for brain structure segmentation in MR images.
1 code implementation • 12 Apr 2018 • Lin Zhang, Neerav Karani, Christine Tanner, Ender Konukoglu
Temporal interpolation of navigator slices an be used to reduce the number of navigator acquisitions without degrading specificity in stacking.