Search Results for author: Oliver Speck

Found 11 papers, 6 papers with code

TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models

no code implementations16 Oct 2021 Soumick Chatterjee, Arnab Das, Chirag Mandal, Budhaditya Mukhopadhyay, Manish Vipinraj, Aniruddh Shukla, Rajatha Nagaraja Rao, Chompunuch Sarasaen, Oliver Speck, Andreas Nürnberger

Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generate visual interpretations and explanations for clinicians to corroborate their clinical findings.

Classification of Brain Tumours in MR Images using Deep Spatiospatial Models

no code implementations28 May 2021 Soumick Chatterjee, Faraz Ahmed Nizamani, Andreas Nürnberger, Oliver Speck

Finally, Pre-trained ResNet Mixed Convolution was observed to be the best model in these experiments, achieving a macro F1-score of 0. 93 and a test accuracy of 96. 98\%, while at the same time being the model with the least computational cost.

Tumour Classification

ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data

2 code implementations16 Mar 2021 Soumick Chatterjee, Mario Breitkopf, Chompunuch Sarasaen, Hadya Yassin, Georg Rose, Andreas Nürnberger, Oliver Speck

It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0. 968$\pm$0. 005) and 17 for radially (0. 962$\pm$0. 012) sampled data.

Image Reconstruction SSIM

Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning

no code implementations28 Nov 2020 Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Steffen Oeltze-Jafra, Andreas Nürnberger, Oliver Speck

Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts.

Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation

1 code implementation20 Nov 2020 Dhanunjaya Mitta, Soumick Chatterjee, Oliver Speck, Andreas Nürnberger

Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors.

Liver Segmentation SSIM

CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation

1 code implementation17 Jan 2020 A. Emre Kavur, N. Sinem Gezer, Mustafa Barış, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savaş Özkan, Bora Baydar, Dmitry Lachinov, Shuo Han, Josef Pauli, Fabian Isensee, Matthias Perkonigg, Rachana Sathish, Ronnie Rajan, Debdoot Sheet, Gurbandurdy Dovletov, Oliver Speck, Andreas Nürnberger, Klaus H. Maier-Hein, Gözde BOZDAĞI AKAR, Gözde Ünal, Oğuz Dicle, M. Alper Selver

The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).

Breathing deformation model -- application to multi-resolution abdominal MRI

no code implementations10 Oct 2019 Chompunuch Sarasaen, Soumick Chatterjee, Mario Breitkopf, Domenico Iuso, Georg Rose, Oliver Speck

This deformation model was then applied to the high resolution images to obtain high resolution images of different breathing phases.

Image Registration

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