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

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

27 June 2020
Zhijian Yang
Junhao Wen
Christos Davatzikos
ArXiv (abs)PDFHTML

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

6 / 6 papers shown
Analyzing heterogeneity in Alzheimer Disease using multimodal normative
  modeling on imaging-based ATN biomarkers
Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkersbioRxiv (bioRxiv), 2023
Sayantan Kumar
Tom Earnest
Braden Yang
Deydeep Kothapalli
Andrew J. Aschenbrenner
...
John Morris
Tammie L. S. Benzinger
Brian A. Gordon
Philip R. O. Payne
Aristeidis Sotiras
187
2
0
04 Apr 2024
Persistent Homological State-Space Estimation of Functional Human Brain
  Networks at Rest
Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest
ID MooK.Chung
Shih-Gu Huang
Ian C. Carroll
Vince D. Calhoun
ID HHillGoldsmith
599
3
0
01 Jan 2022
UCSL : A Machine Learning Expectation-Maximization framework for
  Unsupervised Clustering driven by Supervised Learning
UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised Learning
Robin Louiset
Pietro Gori
Benoit Dufumier
J. Houenou
Antoine Grigis
Edouard Duchesnay
103
2
0
05 Jul 2021
Lattice Paths for Persistent Diagrams
Lattice Paths for Persistent Diagrams
M. Chung
H. Ombao
186
7
0
01 May 2021
Image Synthesis with Adversarial Networks: a Comprehensive Survey and
  Case Studies
Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case StudiesInformation Fusion (Inf. Fusion), 2020
Pourya Shamsolmoali
Masoumeh Zareapoor
Mohammadhadi Shateri
Huiyu Zhou
Ruili Wang
M. E. Celebi
Jie Yang
EGVM
318
153
0
26 Dec 2020
A review of deep learning in medical imaging: Imaging traits, technology
  trends, case studies with progress highlights, and future promises
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisesProceedings of the IEEE (Proc. IEEE), 2020
S. Kevin Zhou
H. Greenspan
Christos Davatzikos
James S. Duncan
Bram van Ginneken
A. Madabhushi
Jerry L. Prince
Daniel Rueckert
Ronald M. Summers
475
842
0
02 Aug 2020
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