283 citations · 293 across the 7 of their papers we have counts for
17 papers
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…
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…