Skip to main content
. Author manuscript; available in PMC: 2020 Dec 3.
Published in final edited form as: Med Image Comput Comput Assist Interv. 2020 Sep 29;12265:3–13. doi: 10.1007/978-3-030-59722-1_1

Fig. 1:

Fig. 1:

Informative channel identification. (a) Given highly multiplexed imaging data, we train (b) a neural network to encode a channel embedding and classify a label (e.g., tumor grade). Then, we measure (c) the classification task channel importance by adopting an interpretation method to the channel embedding. (d) We evaluate our system by comparing the predicted informative channels to expert knowledge, and provide new insights for clinicians and pathologists.