Search Results for author: Krishna Chaitanya

Found 13 papers, 8 papers with code

Explicitly Minimizing the Blur Error of Variational Autoencoders

no code implementations12 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.

Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation

no code implementations17 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.

Image Segmentation Pseudo Label +4

Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation

1 code implementation9 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.

Data Augmentation Image Segmentation +3

Task-agnostic Out-of-Distribution Detection Using Kernel Density Estimation

1 code implementation18 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.

Autonomous Driving Classification +8

Contrastive learning of global and local features for medical image segmentation with limited annotations

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.

Contrastive Learning Data Augmentation +4

Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation

2 code implementations9 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.

Denoising Domain Generalization +5

Semi-Supervised and Task-Driven Data Augmentation

1 code implementation11 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.

Data Augmentation Segmentation

Learning to Segment Medical Images with Scribble-Supervision Alone

no code implementations12 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.

Anatomy Image Segmentation +3

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