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Background  There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on  post-hoc  analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce.    Results  Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by  a priori  statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on  post-hoc  tuning.    Conclusion  Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.    Electronic supplementary material  The online version of this article (10.1186/s12859-018-2184-4) contains supplementary material, which is available to authorized users.

Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction