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Found 4 papers, 0 papers with code
Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification.
Boosting Adversarial Attacks by Leveraging Decision Boundary Information
However, our method still outperforms existing methods when attacking transformers.
Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation
In this paper, we move a step forward and show the existence of a \textbf{training-free} adversarial perturbation under the no-box threat model, which can be successfully used to attack different DNNs in real-time.
Unsupervised Domain Adaptation for the Histopathological Cell Segmentation through Self-Ensembling
Meanwhile, we present a self-ensembling model to consider the source and the target domain together as a semi-supervised segmentation task to reduce the differences of outputs.
