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. Author manuscript; available in PMC: 2023 Aug 31.
Published in final edited form as: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2023 Aug 22;2023:19798–19808. doi: 10.1109/cvpr52729.2023.01896

Figure 4. Visualization of learned TCGA representations using SimCLR and HiDisc.

Figure 4.

We randomly sample patches from the validation set, and visualize these representations using tSNE [52]. Representations on the plots are colored by IDH mutational status. Qualitatively, we can observe that HiDisc forms better representations compared to SimCLR, with clusters within each mutation that corresponds to patient membership. This observation is consistent with the visualizations for the SRH dataset in Figure 3.