Fig. 2 |. Dataset size impact and model introspection.

a, Dataset size plays an important role in achieving clinical-grade MIL classification performance. Training of ResNet34 was performed with datasets of increasing size; for every reported training set size, five models were trained, and the validation errors are reported as box plots (n = 5). This experiment underlies the fact that a large number of slides are necessary for generalization of learning under the MIL assumption. b,c, The prostate model has learned a rich feature representation of histopathology tiles. b, A ResNet34 model trained at 20× was used to obtain the feature embedding before the final classification layer for a random set of tiles in the test set (n = 182,912). The embedding was reduced to two dimensions with t-SNE and plotted using a hexagonal heat map. Top-ranked tiles coming from negative and positive slides are represented by points colored by their tumor probability. c, Tiles corresponding to points in the two-dimensional t-SNE space were randomly sampled from different regions. Abnormal glands are clustered together on the bottom and left sides of the plot. A region of tiles with a tumor probability of ~0.5 contains glands with features suspicious for prostatic adenocarcinoma. Normal glands are clustered on the top left region of the plot.a, Dataset size plays an important role in achieving clinical-grade MIL classification performance. Training of ResNet34 was performed with datasets of increasing size; for every reported training set size, five models were trained, and the validation errors are reported as box plots (n = 5). This experiment underlies the fact that a large number of slides are necessary for generalization of learning under the MIL assumption. b,c, The prostate model has learned a rich feature representation of histopathology tiles. b, A ResNet34 model trained at 20× was used to obtain the feature embedding before the final classification layer for a random set of tiles in the test set (n = 182,912). The embedding was reduced to two dimensions with t-SNE and plotted using a hexagonal heat map. Top-ranked tiles coming from negative and positive slides are represented by points colored by their tumor probability. c, Tiles corresponding to points in the two-dimensional t-SNE space were randomly sampled from different regions. Abnormal glands are clustered together on the bottom and left sides of the plot. A region of tiles with a tumor probability of ~0.5 contains glands with features suspicious for prostatic adenocarcinoma. Normal glands are clustered on the top left region of the plot.