Search Results for author: Dorit Merhof

Found 48 papers, 29 papers with code

SortedAP: Rethinking evaluation metrics for instance segmentation

no code implementations9 Sep 2023 Long Chen, Yuli Wu, Johannes Stegmaier, Dorit Merhof

Designing metrics for evaluating instance segmentation revolves around comprehensively considering object detection and segmentation accuracy.

Instance Segmentation object-detection +2

Semi-supervised Instance Segmentation with a Learned Shape Prior

no code implementations9 Sep 2023 Long Chen, Weiwen Zhang, Yuli Wu, Martin Strauch, Dorit Merhof

To date, most instance segmentation approaches are based on supervised learning that requires a considerable amount of annotated object contours as training ground truth.

Cell Segmentation Instance Segmentation +2

Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation

no code implementations31 Aug 2023 Reza Azad, Leon Niggemeier, Michael Huttemann, Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Yury Velichko, Ulas Bagci, Dorit Merhof

To address these challenges, we introduce the concept of \textbf{Deformable Large Kernel Attention (D-LKA Attention)}, a streamlined attention mechanism employing large convolution kernels to fully appreciate volumetric context.

Image Segmentation Medical Image Segmentation +1

Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers

1 code implementation25 Aug 2023 Reza Azad, Amirhossein Kazerouni, Alaa Sulaiman, Afshin Bozorgpour, Ehsan Khodapanah Aghdam, Abin Jose, Dorit Merhof

Furthermore, to intensify the importance of the boundary information, we impose an additional attention map by creating a Gaussian pyramid on top of the HF components.

Image Segmentation Lesion Segmentation +2

Implicit Neural Representation in Medical Imaging: A Comparative Survey

1 code implementation30 Jul 2023 Amirali Molaei, Amirhossein Aminimehr, Armin Tavakoli, Amirhossein Kazerouni, Bobby Azad, Reza Azad, Dorit Merhof

Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging.

Domain Adaptation Image Reconstruction +1

A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation

no code implementations7 Feb 2023 Yuli Wu, Ivan Karetic, Johannes Stegmaier, Peter Walter, Dorit Merhof

The pre-trained retinal implant model is also a U-Net, which is trained to mimic the biomimetic perceptual model implemented in pulse2percept.

Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach

1 code implementation25 Jan 2023 Reza Azad, Yiwei Jia, Ehsan Khodapanah Aghdam, Julien Cohen-Adad, Dorit Merhof

(3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective.

Image Segmentation Lesion Segmentation +2

Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review

1 code implementation9 Jan 2023 Reza Azad, Amirhossein Kazerouni, Moein Heidari, Ehsan Khodapanah Aghdam, Amirali Molaei, Yiwei Jia, Abin Jose, Rijo Roy, Dorit Merhof

The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision.

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

1 code implementation14 Nov 2022 Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari, Reza Azad, Mohsen Fayyaz, Ilker Hacihaliloglu, Dorit Merhof

Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms.

Denoising Navigate

Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation

1 code implementation30 Oct 2022 Ehsan Khodapanah Aghdam, Reza Azad, Maral Zarvani, Dorit Merhof

We argue that the classical concatenation operation utilized in the skip connection path can be further improved by incorporating an attention mechanism.

Image Segmentation Lesion Segmentation +2

TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation

1 code implementation1 Aug 2022 Reza Azad, Moein Heidari, Moein Shariatnia, Ehsan Khodapanah Aghdam, Sanaz Karimijafarbigloo, Ehsan Adeli, Dorit Merhof

Especially, deep neural networks based on seminal architectures such as U-shaped models with skip-connections or atrous convolution with pyramid pooling have been tailored to a wide range of medical image analysis tasks.

Image Segmentation Medical Image Segmentation +1

TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model

1 code implementation27 Jul 2022 Reza Azad, Mohammad T. AL-Antary, Moein Heidari, Dorit Merhof

In the past few years, convolutional neural networks (CNNs), particularly U-Net, have been the prevailing technique in the medical image processing era.

Image Segmentation Medical Image Segmentation +1

Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach

no code implementations6 Apr 2022 Reza Azad, Moein Heidari, Julien Cohen-Adad, Ehsan Adeli, Dorit Merhof

Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related diseases such as osteoporosis, vertebral fractures, and intervertebral disc herniation.

SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities

1 code implementation6 Apr 2022 Reza Azad, Nika Khosravi, Dorit Merhof

Gliomas are one of the most prevalent types of primary brain tumours, accounting for more than 30\% of all cases and they develop from the glial stem or progenitor cells.

Brain Tumor Segmentation Tumor Segmentation

Medical Image Segmentation on MRI Images with Missing Modalities: A Review

no code implementations11 Mar 2022 Reza Azad, Nika Khosravi, Mohammad Dehghanmanshadi, Julien Cohen-Adad, Dorit Merhof

Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging.

Image Generation Image Segmentation +3

Contextual Attention Network: Transformer Meets U-Net

2 code implementations2 Mar 2022 Reza Azad, Moein Heidari, Yuli Wu, Dorit Merhof

Then, they emphasize the informative regions while taking into account the long-range contextual dependency derived by the Transformer module.

Image Segmentation Medical Image Segmentation +1

Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning

no code implementations16 Dec 2021 Nassim Bouteldja, Barbara Mara Klinkhammer, Tarek Schlaich, Peter Boor, Dorit Merhof

In digital pathology, many image analysis tasks are challenged by the need for large and time-consuming manual data annotations to cope with various sources of variability in the image domain.

Image-to-Image Translation Meta-Learning +2

Multiscale Softmax Cross Entropy for Fovea Localization on Color Fundus Photography

no code implementations8 Dec 2021 Yuli Wu, Peter Walter, Dorit Merhof

Fovea localization is one of the most popular tasks in ophthalmic medical image analysis, where the coordinates of the center point of the macula lutea, i. e. fovea centralis, should be calculated based on color fundus images.

The information content of brain states is explained by structural constraints on state energetics

1 code implementation26 Oct 2021 Leon Weninger, Pragya Srivastava, Dale Zhou, Jason Z. Kim, Eli J. Cornblath, Maxwell A. Bertolero, Ute Habel, Dorit Merhof, Dani S. Bassett

These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence.

High-throughput Phenotyping of Nematode Cysts

no code implementations13 Oct 2021 Long Chen, Matthias Daub, Hans-Georg Luigs, Marcus Jansen, Martin Strauch, Dorit Merhof

The beet cyst nematode (BCN) Heterodera schachtii is a plant pest responsible for crop loss on a global scale.

Instance Segmentation Semantic Segmentation +1

Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations

no code implementations9 Aug 2021 Oliver Rippel, Arnav Chavan, Chucai Lei, Dorit Merhof

In our work, we propose a new method to overcome catastrophic forgetting and thus successfully fine-tune pre-trained representations for AD in the transfer learning setting.

Anomaly Detection Transfer Learning

Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection

4 code implementations28 May 2020 Oliver Rippel, Patrick Mertens, Dorit Merhof

We follow an alternative approach and demonstrate that deep feature representations learned by discriminative models on large natural image datasets are well suited to describe normality and detect even subtle anomalies in a transfer learning setting.

Anomaly Detection Transfer Learning

AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018

1 code implementation20 May 2020 Oliver Rippel, Leon Weninger, Dorit Merhof

Fueled by recent advances in machine learning, there has been tremendous progress in the field of semantic segmentation for the medical image computing community.

3D Semantic Segmentation AutoML +1

An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans

no code implementations23 Apr 2020 Michael Gadermayr, Maximilian Tschuchnig, Laxmi Gupta, Dorit Merhof, Nils Krämer, Daniel Truhn, Burkhard Gess

Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medical applications.


Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints

2 code implementations21 Apr 2020 Long Chen, Martin Strauch, Dorit Merhof

The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which effectively avoids the fusion of objects in a crowd.

Cell Segmentation Instance Segmentation +1

MixNet: Multi-modality Mix Network for Brain Segmentation

1 code implementation21 Apr 2020 Long Chen, Dorit Merhof

Automated brain structure segmentation is important to many clinical quantitative analysis and diagnoses.

Brain Segmentation

Radiomic Feature Stability Analysis based on Probabilistic Segmentations

no code implementations13 Oct 2019 Christoph Haarburger, Justus Schock, Daniel Truhn, Philippe Weitz, Gustav Mueller-Franzes, Leon Weninger, Dorit Merhof

From these segmentations, we extract a high number of plausible feature vectors for each lung tumor and analyze feature variance with respect to the segmentations.

feature selection

Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters

2 code implementations9 Feb 2019 Marcin Kopaczka, Justus Schock, Dorit Merhof

We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms.

Facial Landmark Detection Image Segmentation +3

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) +2

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

Image-based Survival Analysis for Lung Cancer Patients using CNNs

no code implementations29 Aug 2018 Christoph Haarburger, Philippe Weitz, Oliver Rippel, Dorit Merhof

Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features.

Survival Analysis Survival Prediction

Spherical Harmonic Residual Network for Diffusion Signal Harmonization

no code implementations5 Aug 2018 Simon Koppers, Luke Bloy, Jeffrey I. Berman, Chantal M. W. Tax, J. Christopher Edgar, Dorit Merhof

For this purpose, a training database is required, which consist of the same subjects, scanned on different scanners.

DELIMIT PyTorch - An extension for Deep Learning in Diffusion Imaging

1 code implementation4 Aug 2018 Simon Koppers, Dorit Merhof

DELIMIT is a framework extension for deep learning in diffusion imaging, which extends the basic framework PyTorch towards spherical signals.

Context-based Normalization of Histological Stains using Deep Convolutional Features

1 code implementation14 Aug 2017 Daniel Bug, Steffen Schneider, Anne Grote, Eva Oswald, Friedrich Feuerhake, Julia Schüler, Dorit Merhof

While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available.

CNN Cascades for Segmenting Whole Slide Images of the Kidney

no code implementations1 Aug 2017 Michael Gadermayr, Ann-Kathrin Dombrowski, Barbara Mara Klinkhammer, Peter Boor, Dorit Merhof

Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based image analysis systems.

whole slide images

Recycle deep features for better object detection

no code implementations18 Jul 2016 Wei Li, Matthias Breier, Dorit Merhof

Aiming at improving the performance of existing detection algorithms developed for different applications, we propose a region regression-based multi-stage class-agnostic detection pipeline, whereby the existing algorithms are employed for providing the initial detection proposals.

object-detection Object Detection +1

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