Search Results for author: Akshay Pai

Found 21 papers, 7 papers with code

Augmentation based unsupervised domain adaptation

no code implementations23 Feb 2022 Mauricio Orbes-Arteaga, Thomas Varsavsky, Lauge Sorensen, Mads Nielsen, Akshay Pai, Sebastien Ourselin, Marc Modat, M Jorge Cardoso

The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation.

Anomaly Detection Segmentation +1

Lung Segmentation from Chest X-rays using Variational Data Imputation

3 code implementations20 May 2020 Raghavendra Selvan, Erik B. Dam, Nicki S. Detlefsen, Sofus Rischel, Kaining Sheng, Mads Nielsen, Akshay Pai

Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19).

Data Augmentation Image Segmentation +2

One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation

2 code implementations5 Nov 2019 Mathias Perslev, Erik Bjørnager Dam, Akshay Pai, Christian Igel

The system relies on multi-planar data augmentation which facilitates the application of a single 2D architecture based on the familiar U-Net.

Data Augmentation Image Segmentation +4

Knowledge distillation for semi-supervised domain adaptation

no code implementations16 Aug 2019 Mauricio Orbes-Arteaga, Jorge Cardoso, Lauge Sørensen, Christian Igel, Sebastien Ourselin, Marc Modat, Mads Nielsen, Akshay Pai

As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.

Domain Adaptation Knowledge Distillation +1

Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning

no code implementations16 Aug 2019 Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre, Zach Eaton-Rosen, Lewis J. Haddow, Lauge Sørensen, Mads Nielsen, Akshay Pai, Sébastien Ourselin, Marc Modat, Parashkev Nachev, M. Jorge Cardoso

Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to $n$ target domains (as long as there is paired data covering all domains).

Domain Adaptation

Robust parametric modeling of Alzheimer's disease progression

no code implementations14 Aug 2019 Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, Marc Modat, M. Jorge Cardoso, Sébastien Ourselin, Lauge Sørensen

Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric MRI and PET biomarkers, CSF measurements, as well as cognitive tests.

Density Estimation

Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

no code implementations17 Mar 2019 Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Lauge Sørensen

The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i. e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method.

Hippocampus Imputation

The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations13 Jan 2019 Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +3

PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

no code implementations3 Oct 2018 Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Stefan Sommer, Mads Nielsen, Christian Igel, Akshay Pai

For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input.

Image Segmentation Medical Image Segmentation +1

Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

no code implementations20 Aug 2018 Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen, Marc Modat, Sébastien Ourselin, Mads Nielsen, Akshay Pai

Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--.

Imputation Segmentation

Boundary Optimizing Network (BON)

no code implementations8 Jan 2018 Marco Singh, Akshay Pai

Given a classification network, we propose to use a collaborative generative network that produces new synthetic data points in the form of perturbations of original data points.

Deep-Learnt Classification of Light Curves

3 code implementations19 Sep 2017 Ashish Mahabal, Kshiteej Sheth, Fabian Gieseke, Akshay Pai, S. George Djorgovski, Andrew Drake, Matthew Graham, the CSS/CRTS/PTF Collaboration

As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and techniques to classify light curves.

Astronomy Classification +3

A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images

no code implementations1 May 2017 Akshay Pai, Stefan Sommer, Lars Lau Raket, Line Kühnel, Sune Darkner, Lauge Sørensen, Mads Nielsen

Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population variability.

Anatomy Image Registration

A Stochastic Large Deformation Model for Computational Anatomy

no code implementations16 Dec 2016 Alexis Arnaudon, Darryl D. Holm, Akshay Pai, Stefan Sommer

In the study of shapes of human organs using computational anatomy, variations are found to arise from inter-subject anatomical differences, disease-specific effects, and measurement noise.


Most Likely Separation of Intensity and Warping Effects in Image Registration

no code implementations18 Apr 2016 Line Kühnel, Stefan Sommer, Akshay Pai, Lars Lau Raket

This paper introduces a class of mixed-effects models for joint modeling of spatially correlated intensity variation and warping variation in 2D images.

Image Registration

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