7 citations · 7 across the 1 of their papers we have counts for
7 papers · 1 filter
CNN-based Lung CT Registration with Multiple Anatomical Constraints
Alessa Hering, Stephanie Häger, Jan Moltz +3
Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance…
Streaming convolutional neural networks for end-to-end learning with multi-megapixel images
Hans Pinckaers, Bram van Ginneken, Geert Litjens
Due to memory constraints on current hardware, most convolution neural networks (CNN) are trained on sub-megapixel images. For example, most popular datasets in computer vision con…
BIAS: Transparent reporting of biomedical image analysis challenges
Lena Maier-Hein, Annika Reinke, Michal Kozubek +11
The number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common…
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
Guilherme Aresta, Colin Jacobs, Teresa Araújo +4
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is composed of two blocks…
Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
Wouter Bulten, Péter Bándi, Jeffrey Hoven +7
Prostate cancer (PCa) is graded by pathologists by examining the architectural pattern of cancerous epithelial tissue on hematoxylin and eosin (H&E) stained slides. Given the impor…
Why rankings of biomedical image analysis competitions should be interpreted with care
Lena Maier-Hein, Matthias Eisenmann, Annika Reinke +35
International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of c…