766 citations · 1.8k across the 17 of their papers we have counts for
20 papers
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…
Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling
Jianan Chen, Anne L. Martel
Cancer stage is a large determinant of patient prognosis and management in many cancer types, and is often assessed using medical imaging modalities, such as CT and MRI. These medi…
BI-RADS BERT & Using Section Segmentation to Understand Radiology Reports
Grey Kuling, Belinda Curpen, Anne L. Martel
Radiology reports are one of the main forms of communication between radiologists and other clinicians and contain important information for patient care. In order to use this info…
Resource and data efficient self supervised learning
Ozan Ciga, Tony Xu, Anne L. Martel
We investigate the utility of pretraining by contrastive self supervised learning on both natural-scene and medical imaging datasets when the unlabeled dataset size is small, or wh…
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…
Overcoming the limitations of patch-based learning to detect cancer in whole slide images
Ozan Ciga, Tony Xu, Sharon Nofech-Mozes +3
Whole slide images (WSIs) pose unique challenges when training deep learning models. They are very large which makes it necessary to break each image down into smaller patches for…
Self supervised contrastive learning for digital histopathology
Ozan Ciga, Tony Xu, Anne L. Martel
Unsupervised learning has been a long-standing goal of machine learning and is especially important for medical image analysis, where the learning can compensate for the scarcity o…
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…
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…
Interpreting extracted rules from ensemble of trees: Application to computer-aided diagnosis of breast MRI
Cristina Gallego-Ortiz, Anne L. Martel
High predictive performance and ease of use and interpretability are important requirements for the applicability of a computer-aided diagnosis (CAD) to human reading studies. We p…