766 citations · 1.8k across the 17 of their papers we have counts for
6 papers · 1 filter
Learning to segment images with classification labels
Ozan Ciga, Anne L. Martel
Two of the most common tasks in medical imaging are classification and segmentation. Either task requires labeled data annotated by experts, which is scarce and expensive to collec…
Deep neural network models for computational histopathology: A survey
Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to diseases progression and patient survival outcomes. R…
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…
Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model
Jianan Chen, Laurent Milot, Helen M. C. Cheung +1
Quantitative medical image computing (radiomics) has been widely applied to build prediction models from medical images. However, overfitting is a significant issue in conventional…
Intensity augmentation for domain transfer of whole breast segmentation in MRI
Linde S. Hesse, Grey Kuling, Mitko Veta +1
The segmentation of the breast from the chest wall is an important first step in the analysis of breast magnetic resonance images. 3D U-nets have been shown to obtain high segmenta…
Multi-layer Domain Adaptation for Deep Convolutional Networks
Ozan Ciga, Jianan Chen, Anne Martel
Despite their success in many computer vision tasks, convolutional networks tend to require large amounts of labeled data to achieve generalization. Furthermore, the performance is…