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

ABSTRACT:

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 cla

SUBMITTER: Faust K 

PROVIDER: S-EPMC5956828 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature