Search Results for author: Geert Litjens

Found 25 papers, 9 papers with code

Automatic tumour segmentation in H&E-stained whole-slide images of the pancreas

no code implementations1 Dec 2021 Pierpaolo Vendittelli, Esther M. M. Smeets, Geert Litjens

A crucial first step in these pipelines is typically identification and segmentation of the tumour area.

whole slide images

Common Limitations of Image Processing Metrics: A Picture Story

1 code implementation12 Apr 2021 Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Keyvan Farahani, Bram van Ginneken, Ben Glocker, Patrick Godau, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Michael M. Hoffmann, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Alexandros Karargyris, Alan Karthikesalingam, Bernhard Kainz, Emre Kavur, Hannes Kenngott, Jens Kleesiek, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Gorkem Polat, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clarisa Sanchez Gutierrez, Julien Schroeter, Anindo Saha, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Annette Kopp-Schneider, Paul Jäger, Lena Maier-Hein

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.

Instance Segmentation object-detection +2

Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels

1 code implementation5 Jun 2020 Hans Pinckaers, Wouter Bulten, Jeroen van der Laak, Geert Litjens

As such, developing algorithms which do not require manual pixel-wise annotations, but can learn using only the clinical report would be a significant advancement for the field.

Multiple Instance Learning whole slide images

Streaming convolutional neural networks for end-to-end learning with multi-megapixel images

3 code implementations11 Nov 2019 Hans Pinckaers, Bram van Ginneken, Geert Litjens

This method exploits the locality of most operations in modern convolutional neural networks by performing the forward and backward pass on smaller tiles of the image.

Computer Vision

Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification

no code implementations16 May 2019 Koen Dercksen, Wouter Bulten, Geert Litjens

Results show that semi-/unsupervised methods have an advantage over supervised learning when few labels are available.

General Classification

Neural Image Compression for Gigapixel Histopathology Image Analysis

1 code implementation7 Nov 2018 David Tellez, Geert Litjens, Jeroen van der Laak, Francesco Ciompi

Second, a convolutional neural network (CNN) is trained on these compressed image representations to predict image-level labels, avoiding the need for fine-grained manual annotations.

Image Compression Medical Diagnosis

Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

no code implementations17 Aug 2018 David Tellez, Maschenka Balkenhol, Irene Otte-Holler, Rob van de Loo, Rob Vogels, Peter Bult, Carla Wauters, Willem Vreuls, Suzanne Mol, Nico Karssemeijer, Geert Litjens, Jeroen van der Laak, Francesco Ciompi

Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by: (1) noisy and expensive reference standards established by pathologists, (2) lack of generalization due to staining variation across laboratories, and (3) high computational requirements needed to process gigapixel whole-slide images (WSIs).

Data Augmentation Knowledge Distillation +2

Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders

no code implementations19 Apr 2018 Wouter Bulten, Geert Litjens

We propose an unsupervised method using self-clustering convolutional adversarial autoencoders to classify prostate tissue as tumor or non-tumor without any labeled training data.

Training convolutional neural networks with megapixel images

1 code implementation16 Apr 2018 Hans Pinckaers, Geert Litjens

To train deep convolutional neural networks, the input data and the intermediate activations need to be kept in memory to calculate the gradient descent step.

The importance of stain normalization in colorectal tissue classification with convolutional networks

1 code implementation20 Feb 2017 Francesco Ciompi, Oscar Geessink, Babak Ehteshami Bejnordi, Gabriel Silva de Souza, Alexi Baidoshvili, Geert Litjens, Bram van Ginneken, Iris Nagtegaal, Jeroen van der Laak

The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image.

Classification General Classification

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