no code implementations • 3 Sep 2021 • 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 when the diversity within the unlabeled set does not lead to better representations.
no code implementations • 1 Dec 2020 • Ozan Ciga, Tony Xu, Sharon Nofech-Mozes, Shawna Noy, Fang-I Lu, Anne L. Martel
We apply a binary cancer detection network on post neoadjuvant therapy breast cancer WSIs to find the tumor bed outlining the extent of cancer, a task which requires sensitivity and precision across the whole slide.
2 code implementations • 27 Nov 2020 • Ozan Ciga, Tony Xu, Anne L. Martel
In this paper, we use a contrastive self-supervised learning method called SimCLR that achieved state-of-the-art results on natural-scene images and apply this method to digital histopathology by collecting and pretraining on 57 histopathology datasets without any labels.
no code implementations • 28 Dec 2019 • 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.
no code implementations • 28 Dec 2019 • Ozan Ciga, Anne L. Martel
Annotating data for segmentation is generally considered to be more laborious as the annotator has to draw around the boundaries of regions of interest, as opposed to assigning image patches a class label.
no code implementations • 5 Sep 2019 • 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.