128 citations · 156 across the 6 of their papers we have counts for
6 papers
DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images
Ke Yu, Li Sun, Junxiang Chen +3
Large-scale volumetric medical images with annotation are rare, costly, and time prohibitive to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature…
Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN
Li Sun, Junxiang Chen, Yanwu Xu +3
Generative Adversarial Networks (GAN) have many potential medical imaging applications, including data augmentation, domain adaptation, and model explanation. Due to the limited me…
Explanation by Progressive Exaggeration
Sumedha Singla, Brian Pollack, Junxiang Chen +1
As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation…
Robust Ordinal VAE: Employing Noisy Pairwise Comparisons for Disentanglement
Junxiang Chen, Kayhan Batmanghelich
Recent work by Locatello et al. (2018) has shown that an inductive bias is required to disentangle factors of interest in Variational Autoencoder (VAE). Motivated by a real-world p…
Weakly Supervised Disentanglement by Pairwise Similarities
Junxiang Chen, Kayhan Batmanghelich
Recently, researches related to unsupervised disentanglement learning with deep generative models have gained substantial popularity. However, without introducing supervision, ther…
Generative-Discriminative Complementary Learning
Yanwu Xu, Mingming Gong, Junxiang Chen +3
Majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such appr…