Search Results for author: Junhao Wen

Found 14 papers, 6 papers with code

Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns

1 code implementation ICLR 2022 Zhijian Yang, Junhao Wen, Christos Davatzikos

The model first learns a transformation function from normal control (CN) domain to the patient (PT) domain with latent variables controlling transformation directions.

Clustering Representation Learning

Subtyping brain diseases from imaging data

no code implementations16 Feb 2022 Junhao Wen, Erdem Varol, Zhijian Yang, Gyujoon Hwang, Dominique Dwyer, Anahita Fathi Kazerooni, Paris Alexandros Lalousis, Christos Davatzikos

The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment.

MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases

1 code implementation1 Jul 2020 Junhao Wen, Erdem Varol, Ganesh Chand, Aristeidis Sotiras, Christos Davatzikos

There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimers Disease (AD).

Clustering Hippocampus

Smile-GANs: Semi-supervised clustering via GANs for dissecting brain disease heterogeneity from medical images

no code implementations27 Jun 2020 Zhijian Yang, Junhao Wen, Christos Davatzikos

The model was first trained using baseline MRIs from the ADNI2 database and then applied to longitudinal data from ADNI1 and BLSA.


Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective

no code implementations5 Mar 2020 Min Gao, Junwei Zhang, Junliang Yu, Jundong Li, Junhao Wen, Qingyu Xiong

In general, two lines of research have been conducted, and their common ideas can be summarized as follows: (1) for the data noise issue, adversarial perturbations and adversarial sampling-based training often serve as a solution; (2) for the data sparsity issue, data augmentation--implemented by capturing the distribution of real data under the minimax framework--is the primary coping strategy.

Data Augmentation Recommendation Systems

PA-Cache: Evolving Learning-Based Popularity-Aware Content Caching in Edge Networks

no code implementations20 Feb 2020 Qilin Fan, Xiuhua Li, Jian Li, Qiang He, Kai Wang, Junhao Wen

Compared to the conventional content delivery networks, caches in edge networks with smaller sizes usually have to accommodate more bursty requests.

Decision Making

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