766 citations · 1.8k across the 17 of their papers we have counts for
11 papers · 1 filter
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Improving Self-supervised Learning with Hardness-aware Dynamic Curriculum Learning: An Application to Digital Pathology
Chetan L Srinidhi, Anne L Martel
Self-supervised learning (SSL) has recently shown tremendous potential to learn generic visual representations useful for many image analysis tasks. Despite their notable success,…
Self-supervised driven consistency training for annotation efficient histopathology image analysis
Chetan L. Srinidhi, Seung Wook Kim, Fu-Der Chen +1
Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often…
AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastases
Jianan Chen, Helen M. C. Cheung, Laurent Milot +1
Colorectal cancer is one of the most common and lethal cancers and colorectal cancer liver metastases (CRLM) is the major cause of death in patients with colorectal cancer. Multifo…
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…
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…
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…
Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification
Marc Combalia, Veronica Vilaplana
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared w…
Cluster-Based Learning from Weakly Labeled Bags in Digital Pathology
Shazia Akbar, Anne L. Martel
To alleviate the burden of gathering detailed expert annotations when training deep neural networks, we propose a weakly supervised learning approach to recognize metastases in mic…
Transitioning between Convolutional and Fully Connected Layers in Neural Networks
Shazia Akbar, Mohammad Peikari, Sherine Salama +2
Digital pathology has advanced substantially over the last decade however tumor localization continues to be a challenging problem due to highly complex patterns and textures in th…