Search Results for author: Klaus H. Maier-Hein

Found 50 papers, 26 papers with code

Enhancing predictive imaging biomarker discovery through treatment effect analysis

no code implementations4 Jun 2024 Shuhan Xiao, Lukas Klein, Jens Petersen, Philipp Vollmuth, Paul F. Jaeger, Klaus H. Maier-Hein

In response, we present a new task of discovering predictive imaging biomarkers directly from the pre-treatment images to learn relevant image features.

Decision Making

Embarrassingly Simple Scribble Supervision for 3D Medical Segmentation

no code implementations19 Mar 2024 Karol Gotkowski, Carsten Lüth, Paul F. Jäger, Sebastian Ziegler, Lars Krämer, Stefan Denner, Shuhan Xiao, Nico Disch, Klaus H. Maier-Hein, Fabian Isensee

We relate this shortcoming to two major issues: 1) the complex nature of many methods which deeply ties them to the underlying segmentation model, thus preventing a migration to more powerful state-of-the-art models as the field progresses and 2) the lack of a systematic evaluation to validate consistent performance across the broader medical domain, resulting in a lack of trust when applying these methods to new segmentation problems.

Benchmarking Segmentation

DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images

no code implementations12 Mar 2024 Michael Götz, Christian Weber, Franciszek Binczyk, Joanna Polanska, Rafal Tarnawski, Barbara Bobek-Billewicz, Ullrich Köthe, Jens Kleesiek, Bram Stieltjes, Klaus H. Maier-Hein

We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor segmentation.

Domain Adaptation Transfer Learning +1

Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans

no code implementations24 Sep 2023 Fabian Isensee, Klaus H. Maier-Hein

We participate in the AutoPET II challenge by modifying nnU-Net only through its easy to understand and modify 'nnUNetPlans. json' file.

RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

no code implementations14 Sep 2023 Gregor Koehler, Tassilo Wald, Constantin Ulrich, David Zimmerer, Paul F. Jaeger, Jörg K. H. Franke, Simon Kohl, Fabian Isensee, Klaus H. Maier-Hein

Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision.

Decision Making Image Segmentation +3

Taming Detection Transformers for Medical Object Detection

no code implementations27 Jun 2023 Marc K. Ickler, Michael Baumgartner, Saikat Roy, Tassilo Wald, Klaus H. Maier-Hein

The accurate detection of suspicious regions in medical images is an error-prone and time-consuming process required by many routinely performed diagnostic procedures.

Medical Object Detection Object +1

MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation

1 code implementation25 Mar 2023 Constantin Ulrich, Fabian Isensee, Tassilo Wald, Maximilian Zenk, Michael Baumgartner, Klaus H. Maier-Hein

Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance.

Image Segmentation Lesion Segmentation +3

Accurate Detection of Mediastinal Lesions with nnDetection

no code implementations20 Mar 2023 Michael Baumgartner, Peter M. Full, Klaus H. Maier-Hein

The accurate detection of mediastinal lesions is one of the rarely explored medical object detection problems.

Medical Object Detection object-detection

ParticleSeg3D: A Scalable Out-of-the-Box Deep Learning Segmentation Solution for Individual Particle Characterization from Micro CT Images in Mineral Processing and Recycling

1 code implementation30 Jan 2023 Karol Gotkowski, Shuvam Gupta, Jose R. A. Godinho, Camila G. S. Tochtrop, Klaus H. Maier-Hein, Fabian Isensee

Our approach is based on the powerful nnU-Net framework, introduces a particle size normalization, uses a border-core representation to enable instance segmentation, and is trained with a large dataset containing particles of numerous different sizes, shapes, and compositions of various materials.

Computed Tomography (CT) Instance Segmentation +2

CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization

no code implementations5 Jan 2023 Carsten T. Lüth, David Zimmerer, Gregor Koehler, Paul F. Jaeger, Fabian Isensee, Jens Petersen, Klaus H. Maier-Hein

By utilizing the representations of contrastive learning, we aim to fix the over-fixation on low-level features and learn more semantic-rich representations.

Contrastive Learning Unsupervised Anomaly Detection

Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding

no code implementations7 Oct 2022 Constantin Seibold, Simon Reiß, Saquib Sarfraz, Matthias A. Fink, Victoria Mayer, Jan Sellner, Moon Sung Kim, Klaus H. Maier-Hein, Jens Kleesiek, Rainer Stiefelhagen

To exploit anatomical structures in this scenario, we present a sophisticated automatic pipeline to gather and integrate human bodily structures from computed tomography datasets, which we incorporate in our PAXRay: A Projected dataset for the segmentation of Anatomical structures in X-Ray data.

Anatomy Phrase Grounding

Extending nnU-Net is all you need

no code implementations23 Aug 2022 Fabian Isensee, Constantin Ulrich, Tassilo Wald, Klaus H. Maier-Hein

Semantic segmentation is one of the most popular research areas in medical image computing.

Segmentation Semantic Segmentation +1

Continuous-Time Deep Glioma Growth Models

2 code implementations23 Jun 2021 Jens Petersen, Fabian Isensee, Gregor Köhler, Paul F. Jäger, David Zimmerer, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Vollmuth, Klaus H. Maier-Hein

The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy.

Time Series Time Series Analysis +1

GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data

1 code implementation9 Jun 2021 Jens Petersen, Gregor Köhler, David Zimmerer, Fabian Isensee, Paul F. Jäger, Klaus H. Maier-Hein

Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points.

Time Series Time Series Analysis

nnDetection: A Self-configuring Method for Medical Object Detection

1 code implementation1 Jun 2021 Michael Baumgartner, Paul F. Jaeger, Fabian Isensee, Klaus H. Maier-Hein

Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e. g. pixels.

Image Segmentation Medical Object Detection +3

A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients

no code implementations28 Nov 2019 David Zimmerer, Jens Petersen, Simon A. A. Kohl, Klaus H. Maier-Hein

Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions.

Anomaly Detection

ModelHub.AI: Dissemination Platform for Deep Learning Models

no code implementations26 Nov 2019 Ahmed Hosny, Michael Schwier, Christoph Berger, Evin P Örnek, Mehmet Turan, Phi V Tran, Leon Weninger, Fabian Isensee, Klaus H. Maier-Hein, Richard McKinley, Michael T Lu, Udo Hoffmann, Bjoern Menze, Spyridon Bakas, Andriy Fedorov, Hugo JWL Aerts

Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others.

Benchmarking

An attempt at beating the 3D U-Net

1 code implementation6 Aug 2019 Fabian Isensee, Klaus H. Maier-Hein

The U-Net is arguably the most successful segmentation architecture in the medical domain.

Segmentation Tumor Segmentation

Reg R-CNN: Lesion Detection and Grading under Noisy Labels

2 code implementations22 Jul 2019 Gregor N. Ramien, Paul F. Jaeger, Simon A. A. Kohl, Klaus H. Maier-Hein

To this end, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model.

General Classification Lesion Detection +2

Deep Probabilistic Modeling of Glioma Growth

1 code implementation9 Jul 2019 Jens Petersen, Paul F. Jäger, Fabian Isensee, Simon A. A. Kohl, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Kickingereder, Klaus H. Maier-Hein

Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the associated parameters.

Representation Learning

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

4 code implementations30 May 2019 Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein, Danilo Jimenez Rezende, S. M. Ali Eslami, Pushmeet Kohli, Andrew Zisserman, Olaf Ronneberger

Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation.

Inductive Bias Instance Segmentation +3

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

5 code implementations17 Apr 2019 Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl, Jens Petersen, Klaus H. Maier-Hein

Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.

Image Segmentation Medical Image Segmentation +2

Combined tract segmentation and orientation mapping for bundle-specific tractography

2 code implementations29 Jan 2019 Jakob Wasserthal, Peter Neher, Dusan Hirjak, Klaus H. Maier-Hein

It is based on a learned mapping from the original fiber orientation distribution function (FOD) peaks to tract specific peaks, called tract orientation maps.

Clustering

Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

6 code implementations21 Nov 2018 Paul F. Jaeger, Simon A. A. Kohl, Sebastian Bickelhaupt, Fabian Isensee, Tristan Anselm Kuder, Heinz-Peter Schlemmer, Klaus H. Maier-Hein

The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors.

Medical Object Detection Object +4

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

No New-Net

no code implementations27 Sep 2018 Fabian Isensee, Philipp Kickingereder, Wolfgang Wick, Martin Bendszus, Klaus H. Maier-Hein

By incorporating additional measures such as region based training, additional training data, a simple postprocessing technique and a combination of loss functions, we obtain Dice scores of 77. 88, 87. 81 and 80. 62, and Hausdorff Distances (95th percentile) of 2. 90, 6. 03 and 5. 08 for the enhancing tumor, whole tumor and tumor core, respectively on the test data.

Tract orientation mapping for bundle-specific tractography

2 code implementations14 Jun 2018 Jakob Wasserthal, Peter F. Neher, Klaus H. Maier-Hein

While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms is time-consuming, requires expert knowledge and is hard to reproduce.

Clustering Decoder

A Probabilistic U-Net for Segmentation of Ambiguous Images

9 code implementations NeurIPS 2018 Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger

To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.

Decision Making Segmentation +1

TractSeg - Fast and accurate white matter tract segmentation

3 code implementations18 May 2018 Jakob Wasserthal, Peter Neher, Klaus H. Maier-Hein

The individual course of white matter fiber tracts is an important key for analysis of white matter characteristics in healthy and diseased brains.

Clustering Image Registration

Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features

1 code implementation3 Jul 2017 Fabian Isensee, Paul Jaeger, Peter M. Full, Ivo Wolf, Sandy Engelhardt, Klaus H. Maier-Hein

We evaluated our method on the ACDC dataset (4 pathology groups, 1 healthy group) and achieve dice scores of 0. 945 (LVC), 0. 908 (RVC) and 0. 905 (LVM) in a cross-validation over the training set (100 cases) and 0. 950 (LVC), 0. 923 (RVC) and 0. 911 (LVM) on the test set (50 cases).

General Classification Segmentation +2

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