no code implementations • 21 Nov 2014 • Mitko Veta, Paul J. van Diest, Stefan M. Willems, Haibo Wang, Anant Madabhushi, Angel Cruz-Roa, Fabio Gonzalez, Anders B. L. Larsen, Jacob S. Vestergaard, Anders B. Dahl, Dan C. Cireşan, Jürgen Schmidhuber, Alessandro Giusti, Luca M. Gambardella, F. Boray Tek, Thomas Walter, Ching-Wei Wang, Satoshi Kondo, Bogdan J. Matuszewski, Frederic Precioso, Violet Snell, Josef Kittler, Teofilo E. de Campos, Adnan M. Khan, Nasir M. Rajpoot, Evdokia Arkoumani, Miangela M. Lacle, Max A. Viergever, Josien P. W. Pluim
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers.
no code implementations • 20 Jun 2016 • Mitko Veta, Paul J. van Diest, Josien P. W. Pluim
We hypothesize that given an image of a tumor region with known nuclei locations, the area of the individual nuclei and region statistics such as the MNA can be reliably computed directly from the image data by employing a machine learning model, without the intermediate step of nuclei segmentation.
1 code implementation • 12 Jun 2017 • Veronika Cheplygina, Pim Moeskops, Mitko Veta, Behdad Dasht Bozorg, Josien Pluim
Supervised learning is ubiquitous in medical image analysis.
no code implementations • 11 Jul 2017 • Pim Moeskops, Mitko Veta, Maxime W. Lafarge, Koen A. J. Eppenhof, Josien P. W. Pluim
To this end, we include an additional loss function that motivates the network to generate segmentations that are difficult to distinguish from manual segmentations.
no code implementations • 19 Jul 2017 • Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta
Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters.
no code implementations • 10 Jan 2018 • Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta
Histological images are obtained by transmitting light through a tissue specimen that has been stained in order to produce contrast.
1 code implementation • 10 Apr 2018 • Erik J. Bekkers, Maxime W. Lafarge, Mitko Veta, Koen AJ Eppenhof, Josien PW Pluim, Remco Duits
We propose a framework for rotation and translation covariant deep learning using $SE(2)$ group convolutions.
no code implementations • 22 Jul 2018 • Mitko Veta, Yujing J. Heng, Nikolas Stathonikos, Babak Ehteshami Bejnordi, Francisco Beca, Thomas Wollmann, Karl Rohr, Manan A. Shah, Dayong Wang, Mikael Rousson, Martin Hedlund, David Tellez, Francesco Ciompi, Erwan Zerhouni, David Lanyi, Matheus Viana, Vassili Kovalev, Vitali Liauchuk, Hady Ahmady Phoulady, Talha Qaiser, Simon Graham, Nasir Rajpoot, Erik Sjöblom, Jesper Molin, Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Zhipeng Jia, Eric I-Chao Chang, Yan Xu, Andrew H. Beck, Paul J. van Diest, Josien P. W. Pluim
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
no code implementations • 27 Jul 2019 • Cian M. Scannell, Piet van den Bosch, Amedeo Chiribiri, Jack Lee, Marcel Breeuwer, Mitko Veta
The quantification of myocardial perfusion MRI has the potential to provide a fast, automated and user-independent assessment of myocardial ischaemia.
no code implementations • 5 Sep 2019 • Linde S. Hesse, Grey Kuling, Mitko Veta, Anne L. Martel
Our results show that using intensity augmentation in addition to geometric augmentation is a suitable method to overcome the intensity domain shift and we expect it to be useful for a wide range of segmentation tasks.
no code implementations • 31 Oct 2019 • Suzanne C. Wetstein, Allison M Onken, Christina Luffman, Gabrielle M Baker, Michael E Pyle, Kevin H Kensler, Ying Liu, Bart Bakker, Ruud Vlutters, Marinus B van Leeuwen, Laura C Collins, Stuart J Schnitt, Josien PW Pluim, Rulla M. Tamimi, Yujing J. Heng, Mitko Veta
A first set of 92 WSIs was annotated for TDLUs, acini and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue.
no code implementations • 22 Nov 2019 • Friso G. Heslinga, Josien P. W. Pluim, A. J. H. M. Houben, Miranda T. Schram, Ronald M. A. Henry, Coen D. A. Stehouwer, Marleen J. van Greevenbroek, Tos T. J. M. Berendschot, Mitko Veta
We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC).
1 code implementation • 20 Feb 2020 • Maxime W. Lafarge, Erik J. Bekkers, Josien P. W. Pluim, Remco Duits, Mitko Veta
This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.
Ranked #5 on Breast Tumour Classification on PCam
no code implementations • 24 Apr 2020 • Friso G. Heslinga, Mark Alberti, Josien P. W. Pluim, Javier Cabrerizo, Mitko Veta
A second DMEK expert annotated the test set to determine inter-rater performance.
1 code implementation • 26 Apr 2020 • Zhaohan Xiong, Qing Xia, Zhiqiang Hu, Ning Huang, Cheng Bian, Yefeng Zheng, Sulaiman Vesal, Nishant Ravikumar, Andreas Maier, Xin Yang, Pheng-Ann Heng, Dong Ni, Caizi Li, Qianqian Tong, Weixin Si, Elodie Puybareau, Younes Khoudli, Thierry Geraud, Chen Chen, Wenjia Bai, Daniel Rueckert, Lingchao Xu, Xiahai Zhuang, Xinzhe Luo, Shuman Jia, Maxime Sermesant, Yashu Liu, Kuanquan Wang, Davide Borra, Alessandro Masci, Cristiana Corsi, Coen de Vente, Mitko Veta, Rashed Karim, Chandrakanth Jayachandran Preetha, Sandy Engelhardt, Menyun Qiao, Yuanyuan Wang, Qian Tao, Marta Nunez-Garcia, Oscar Camara, Nicolo Savioli, Pablo Lamata, Jichao Zhao
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
1 code implementation • 11 Jun 2020 • Gerda Bortsova, Cristina González-Gonzalo, Suzanne C. Wetstein, Florian Dubost, Ioannis Katramados, Laurens Hogeweg, Bart Liefers, Bram van Ginneken, Josien P. W. Pluim, Mitko Veta, Clara I. Sánchez, Marleen de Bruijne
Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model.
1 code implementation • 10 Jul 2020 • Christof A. Bertram, Mitko Veta, Christian Marzahl, Nikolas Stathonikos, Andreas Maier, Robert Klopfleisch, Marc Aubreville
To date, some datasets on mitotic figures are available and were used for development of promising deep learning-based algorithms.
no code implementations • 26 Aug 2020 • Maxime W. Lafarge, Josien P. W. Pluim, Mitko Veta
However, some generative factors that cause irrelevant variations in images can potentially get entangled in such a learned representation causing the risk of negatively affecting any subsequent use.
1 code implementation • 26 Aug 2020 • Cian M. Scannell, Amedeo Chiribiri, Mitko Veta
Deep learning has shown the potential to automate the requisite cardiac structure segmentation.
no code implementations • 7 Oct 2020 • Suzanne C. Wetstein, Nikolas Stathonikos, Josien P. W. Pluim, Yujing J. Heng, Natalie D. ter Hoeve, Celien P. H. Vreuls, Paul J. van Diest, Mitko Veta
In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers.
1 code implementation • 25 Nov 2020 • Rudolf L. M. van Herten, Amedeo Chiribiri, Marcel Breeuwer, Mitko Veta, Cian M. Scannell
This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters.
no code implementations • 15 Feb 2021 • Friso G. Heslinga, Ruben T. Lucassen, Myrthe A. van den Berg, Luuk van der Hoek, Josien P. W. Pluim, Javier Cabrerizo, Mark Alberti, Mitko Veta
In this research, deep learning is used to automatically delineate the corneal interfaces and measure corneal thickness with high accuracy in post-DMEK AS-OCT B-scans.
2 code implementations • 30 Mar 2021 • Marc Aubreville, Christof Bertram, Mitko Veta, Robert Klopfleisch, Nikolas Stathonikos, Katharina Breininger, Natalie ter Hoeve, Francesco Ciompi, Andreas Maier
Hypothesizing that the scanner device plays a decisive role in this effect, we evaluated the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner.
1 code implementation • 27 Sep 2021 • Didier R. P. R. M. Lustermans, Sina Amirrajab, Mitko Veta, Marcel Breeuwer, Cian M. Scannell
The mean DSC per-subject on the challenge test set, for the cascaded pipeline augmented by synthetic generated data, was 0. 86 (0. 03) and 0. 67 (0. 29) for myocardium and scar, respectively.
no code implementations • 6 Apr 2022 • Marc Aubreville, Nikolas Stathonikos, Christof A. Bertram, Robert Klopleisch, Natalie ter Hoeve, Francesco Ciompi, Frauke Wilm, Christian Marzahl, Taryn A. Donovan, Andreas Maier, Jack Breen, Nishant Ravikumar, Youjin Chung, Jinah Park, Ramin Nateghi, Fattaneh Pourakpour, Rutger H. J. Fick, Saima Ben Hadj, Mostafa Jahanifar, Nasir Rajpoot, Jakob Dexl, Thomas Wittenberg, Satoshi Kondo, Maxime W. Lafarge, Viktor H. Koelzer, Jingtang Liang, YuBo Wang, Xi Long, Jingxin Liu, Salar Razavi, April Khademi, Sen yang, Xiyue Wang, Mitko Veta, Katharina Breininger
The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms.
no code implementations • 16 Jan 2023 • Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni, Zhang Li, Tao Tan, Abhir Bhalerao, Jiabo Ma, Jiamei Sun, Johnathan Pocock, Josien P. W. Pluim, Navid Alemi Koohbanani, Raja Muhammad Saad Bashir, Shan E Ahmed Raza, Sibo Liu, Simon Graham, Suzanne Wetstein, Syed Ali Khurram, Thomas Watson, Nasir Rajpoot, Mitko Veta, Francesco Ciompi
Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.
1 code implementation • 15 Feb 2023 • Ruben T. Lucassen, Mohammad H. Jafari, Nicole M. Duggan, Nick Jowkar, Alireza Mehrtash, Chanel Fischetti, Denie Bernier, Kira Prentice, Erik P. Duhaime, Mike Jin, Purang Abolmaesumi, Friso G. Heslinga, Mitko Veta, Maria A. Duran-Mendicuti, Sarah Frisken, Paul B. Shyn, Alexandra J. Golby, Edward Boyer, William M. Wells, Andrew J. Goldsmith, Tina Kapur
B-line artifacts in LUS videos are key findings associated with pulmonary congestion.
1 code implementation • 5 Jun 2023 • Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab, Gerhard S. Drenthen, Mitko Veta, Jacobus F. A. Jansen, Marcel Breeuwer, Ruud J. G. van Sloun
This work proposes a method to accelerate the acquisition of high-quality edited magnetic resonance spectroscopy (MRS) scans using machine learning models taking the sample covariance matrix as input.
no code implementations • 27 Sep 2023 • Marc Aubreville, Nikolas Stathonikos, Taryn A. Donovan, Robert Klopfleisch, Jonathan Ganz, Jonas Ammeling, Frauke Wilm, Mitko Veta, Samir Jabari, Markus Eckstein, Jonas Annuscheit, Christian Krumnow, Engin Bozaba, Sercan Cayir, Hongyan Gu, Xiang 'Anthony' Chen, Mostafa Jahanifar, Adam Shephard, Satoshi Kondo, Satoshi Kasai, Sujatha Kotte, VG Saipradeep, Maxime W. Lafarge, Viktor H. Koelzer, Ziyue Wang, Yongbing Zhang, Sen yang, Xiyue Wang, Katharina Breininger, Christof A. Bertram
The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains.
1 code implementation • 24 Jan 2024 • Ruben T. Lucassen, Willeke A. M. Blokx, Mitko Veta
Our results demonstrate that the proposed model can accurately segment H&E stained tissue cross-sections and pen markings in WSIs while being robust to many common slide and scanning artifacts.
no code implementations • 14 Mar 2024 • Hong Liu, Haosen Yang, Paul J. van Diest, Josien P. W. Pluim, Mitko Veta
In particular, our model outperforms SAM by 4. 1 and 2. 5 percent points on a ductal carcinoma in situ (DCIS) segmentation tasks and breast cancer metastasis segmentation task (CAMELYON16 dataset).