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Contrastive learning for unsupervised medical image clustering and reconstruction. (arXiv:2209.12005v1 [cs.CV])
Sept. 27, 2022, 1:12 a.m. | Matteo Ferrante, Tommaso Boccato, Simeon Spasov, Andrea Duggento, Nicola Toschi
cs.CV updates on arXiv.org arxiv.org
The lack of large labeled medical imaging datasets, along with significant
inter-individual variability compared to clinically established disease
classes, poses significant challenges in exploiting medical imaging information
in a precision medicine paradigm, where in principle dense patient-specific
data can be employed to formulate individual predictions and/or stratify
patients into finer-grained groups which may follow more homogeneous
trajectories and therefore empower clinical trials. In order to efficiently
explore the effective degrees of freedom underlying variability in medical
images in an unsupervised …
More from arxiv.org / cs.CV updates on arXiv.org
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