Jun Zhang

TL;DR: This paper develops a self-supervised learning mechanism to train the feature extractor based on a combination model of variational autoencoder and generative adversarial network (VAE-GAN) and proposes a novel instance-level feature selection method to select the discriminative instance features.

TL;DR: Wang et al. as discussed by the authors proposed a hybrid model (TransPath) which is pre-trained in an SSL manner on massively unlabeled histopathological images to discover the inherent image property and capture domain-specific feature embedding.

TL;DR: Zhang et al. as discussed by the authors proposed a deformable transformer-based multi-instance learning (MIL) model for histopathological image classification and prediction, which enables the model to update each instance feature by globally aggregating instance features in a bag simultaneously and encoding position context information of instances during bag representation learning.

TL;DR: Wang et al. as discussed by the authors proposed a semi-relevant contrastive learning (SRCL) strategy to align multiple positive instances with similar visual concepts, which increases the diversity of positives and then results in more informative representations.

TL;DR: Wang et al. as mentioned in this paper proposed a retrieval with clustering-guided contrastive learning (RetCCL) framework for robust and accurate WSI-level image retrieval, which integrates a novel self-supervised feature learning method and a global ranking and aggregation algorithm for much improved performance.