no code implementations • 12 Apr 2023 • Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya, Ertunc Erdil, Ender Konukoglu
Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have been trained on.
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.
no code implementations • 30 Mar 2021 • Alexis Perakis, Ali Gorji, Samriddhi Jain, Krishna Chaitanya, Simone Rizza, Ender Konukoglu
Learning robust representations to discriminate cell phenotypes based on microscopy images is important for drug discovery.
no code implementations • 6 Mar 2021 • Hongwei Li, Fei-Fei Xue, Krishna Chaitanya, Shengda Luo, Ivan Ezhov, Benedikt Wiestler, JianGuo Zhang, Bjoern Menze
Radiomic representations can quantify properties of regions of interest in medical image data.
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 • 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.
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.
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.
3 code implementations • 7 Jun 2019 • Christian F. Baumgartner, Kerem C. Tezcan, Krishna Chaitanya, Andreas M. Hötker, Urs J. Muehlematter, Khoschy Schawkat, Anton S. Becker, Olivio Donati, Ender Konukoglu
Segmentation of anatomical structures and pathologies is inherently ambiguous.
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.
no code implementations • 12 Jul 2018 • Yigit B. Can, Krishna Chaitanya, Basil Mustafa, Lisa M. Koch, Ender Konukoglu, Christian F. Baumgartner
We find that the networks trained on scribbles suffer from a remarkably small degradation in Dice of only 2. 9% (cardiac) and 4. 5% (prostate) with respect to a network trained on full annotations.
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.