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In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer.
In addition to EPISURG, we used three public datasets comprising 1813 preoperative MR images for training.
Building on these insights and on advances in self-supervised learning, we propose a transfer learning approach which constructs a metric embedding that clusters unlabeled prototypical samples and their augmentations closely together.
Thus, we seek to harness SSL for GNNs to fully exploit the unlabeled data.
We first elaborate three mechanisms to incorporate self-supervision into GCNs, analyze the limitations of pretraining & finetuning and self-training, and proceed to focus on multi-task learning.
From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
#2 best model for Self-Supervised Image Classification on ImageNet
We propose a novel self-supervised semi-supervised learning approach for conditional Generative Adversarial Networks (GANs).
Self-supervised representation learning adopts self-defined signals as supervision and uses the learned representation for downstream tasks, such as masked language modeling (e. g., BERT) for natural language processing and contrastive visual representation learning (e. g., SimCLR) for computer vision applications.