Search Results for author: Andreas Nürnberger

Found 27 papers, 21 papers with code

PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation

2 code implementations25 Dec 2023 Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra, Hendrik Mattern, Gábor Janiga, Oliver Speck, Andreas Nürnberger, Sahani Pathiraja

In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets.

Decision Making Decoder +4

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

1 code implementation30 Aug 2023 Jianning Li, Zongwei Zhou, Jiancheng Yang, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Chongyu Qu, Tiezheng Zhang, Xiaoxi Chen, Wenxuan Li, Marek Wodzinski, Paul Friedrich, Kangxian Xie, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, Gian Marco Melito, Viet Duc Vu, Afaque R. Memon, Christopher Schlachta, Sandrine de Ribaupierre, Rajnikant Patel, Roy Eagleson, Xiaojun Chen, Heinrich Mächler, Jan Stefan Kirschke, Ezequiel de la Rosa, Patrick Ferdinand Christ, Hongwei Bran Li, David G. Ellis, Michele R. Aizenberg, Sergios Gatidis, Thomas Küstner, Nadya Shusharina, Nicholas Heller, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Anjany Sekuboyina, Maximilian Löffler, Hans Liebl, Reuben Dorent, Tom Vercauteren, Jonathan Shapey, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Achraf Ben-Hamadou, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Federico Bolelli, Costantino Grana, Luca Lumetti, Hamidreza Salehi, Jun Ma, Yao Zhang, Ramtin Gharleghi, Susann Beier, Arcot Sowmya, Eduardo A. Garza-Villarreal, Thania Balducci, Diego Angeles-Valdez, Roberto Souza, Leticia Rittner, Richard Frayne, Yuanfeng Ji, Vincenzo Ferrari, Soumick Chatterjee, Florian Dubost, Stefanie Schreiber, Hendrik Mattern, Oliver Speck, Daniel Haehn, Christoph John, Andreas Nürnberger, João Pedrosa, Carlos Ferreira, Guilherme Aresta, António Cunha, Aurélio Campilho, Yannick Suter, Jose Garcia, Alain Lalande, Vicky Vandenbossche, Aline Van Oevelen, Kate Duquesne, Hamza Mekhzoum, Jef Vandemeulebroucke, Emmanuel Audenaert, Claudia Krebs, Timo Van Leeuwen, Evie Vereecke, Hauke Heidemeyer, Rainer Röhrig, Frank Hölzle, Vahid Badeli, Kathrin Krieger, Matthias Gunzer, Jianxu Chen, Timo van Meegdenburg, Amin Dada, Miriam Balzer, Jana Fragemann, Frederic Jonske, Moritz Rempe, Stanislav Malorodov, Fin H. Bahnsen, Constantin Seibold, Alexander Jaus, Zdravko Marinov, Paul F. Jaeger, Rainer Stiefelhagen, Ana Sofia Santos, Mariana Lindo, André Ferreira, Victor Alves, Michael Kamp, Amr Abourayya, Felix Nensa, Fabian Hörst, Alexander Brehmer, Lukas Heine, Yannik Hanusrichter, Martin Weßling, Marcel Dudda, Lars E. Podleska, Matthias A. Fink, Julius Keyl, Konstantinos Tserpes, Moon-Sung Kim, Shireen Elhabian, Hans Lamecker, Dženan Zukić, Beatriz Paniagua, Christian Wachinger, Martin Urschler, Luc Duong, Jakob Wasserthal, Peter F. Hoyer, Oliver Basu, Thomas Maal, Max J. H. Witjes, Gregor Schiele, Ti-chiun Chang, Seyed-Ahmad Ahmadi, Ping Luo, Bjoern Menze, Mauricio Reyes, Thomas M. Deserno, Christos Davatzikos, Behrus Puladi, Pascal Fua, Alan L. Yuille, Jens Kleesiek, Jan Egger

For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.

Anatomy Mixed Reality

Complex Network for Complex Problems: A comparative study of CNN and Complex-valued CNN

1 code implementation9 Feb 2023 Soumick Chatterjee, Pavan Tummala, Oliver Speck, Andreas Nürnberger

Although some comparisons of CNNs and CV-CNNs for different tasks have been performed in the past, a large-scale investigation comparing different models operating on different tasks has not been conducted.

Tumour Classification

Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging

1 code implementation20 Jul 2022 Hana Haseljić, Soumick Chatterjee, Robert Frysch, Vojtěch Kulvait, Vladimir Semshchikov, Bennet Hensen, Frank Wacker, Inga Brüsch, Thomas Werncke, Oliver Speck, Andreas Nürnberger, Georg Rose

This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.

Liver Segmentation Segmentation

Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification

1 code implementation10 Jun 2022 Soumick Chatterjee, Hadya Yassin, Florian Dubost, Andreas Nürnberger, Oliver Speck

The models are trained by using the input image and only the classification labels as ground-truth in a supervised fashion - without using any information about the location of the region of interest (i. e. the segmentation labels), making the segmentation training of the models weakly-supervised through classification labels.

Classification Decision Making +3

Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction

1 code implementation11 May 2022 Philipp Ernst, Soumick Chatterjee, Georg Rose, Andreas Nürnberger

In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices.

Dual Branch Prior-SegNet: CNN for Interventional CBCT using Planning Scan and Auxiliary Segmentation Loss

1 code implementation11 May 2022 Philipp Ernst, Suhita Ghosh, Georg Rose, Andreas Nürnberger

This paper proposes an extension to the Dual Branch Prior-Net for sparse view interventional CBCT reconstruction incorporating a high quality planning scan.

DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI

1 code implementation10 Feb 2022 Soumick Chatterjee, Chompunuch Sarasaen, Georg Rose, Andreas Nürnberger, Oliver Speck

However, such MRI acquisition with high temporal resolution suffers from limited spatial resolution - also known as the spatio-temporal trade-off of dynamic MRI.

SSIM Super-Resolution

Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction

1 code implementation26 Dec 2021 Philipp Ernst, Soumick Chatterjee, Georg Rose, Oliver Speck, Andreas Nürnberger

However, the undersampled reconstruction problem for these two modalities was always considered as two different problems and tackled separately by different research works.

MRI Reconstruction SSIM

Dual Skip Connections Minimize the False Positive Rate of Lung Nodule Detection in CT images

no code implementations25 Oct 2021 Jiahua Xu, Philipp Ernst, Tung Lung Liu, Andreas Nürnberger

Pulmonary cancer is one of the most commonly diagnosed and fatal cancers and is often diagnosed by incidental findings on computed tomography.

Lesion Detection Lung Nodule Detection

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

1 code implementation16 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.

Manually Annotated Spelling Error Corpus for Amharic

1 code implementation25 Jun 2021 Andargachew Mekonnen Gezmu, Tirufat Tesifaye Lema, Binyam Ephrem Seyoum, Andreas Nürnberger

This paper presents a manually annotated spelling error corpus for Amharic, lingua franca in Ethiopia.

Classification of Brain Tumours in MR Images using Deep Spatiospatial Models

1 code implementation28 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

Extended Parallel Corpus for Amharic-English Machine Translation

no code implementations LREC 2022 Andargachew Mekonnen Gezmu, Andreas Nürnberger, Tesfaye Bayu Bati

In the automatic evaluation, neural machine translation models outperform statistical machine translation models by approximately six to seven Bilingual Evaluation Understudy (BLEU) points.

Language Modelling Machine Translation +1

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

4 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

1 code implementation28 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 Segmentation +1

DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data

3 code implementations18 Jun 2020 Soumick Chatterjee, Kartik Prabhu, Mahantesh Pattadkal, Gerda Bortsova, Chompunuch Sarasaen, Florian Dubost, Hendrik Mattern, Marleen de Bruijne, Oliver Speck, Andreas Nürnberger

The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance.

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