Fig. 3 | BMC Bioinformatics

Fig. 3

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

Fig. 3

Visualization of CNN-based histological data structure and classification using t-SNE. a t-SNE plot showing the planar representation of the internal high-dimensional organization of the 13 trained tissue classes within the CNN’s final hidden layer. 350–600 training tiles from each class are plotted so that each point within the t-SNE represents a 1024 × 1024 pixel training image. Tiles belonging to each class are labeled with a unique colour for convenience. Insets show representative images from each cluster/class. b Dimensionality reduction techniques (like t-SNE) position data so that points close together represents images the CNN perceives as have a similar pattern. This plot therefore allows visualization of what classes the computer perceives to be closely related. Learned features appear to qualitatively organize in a biology-inspired manner similar to the framework shown in Fig. 1b. In addition to anuclear (yellow region), normal (red region) and lesional (blue region) tissue regions, there is an additional trend towards cohesive lesions (meningioma and metastasis) being arranged close together as one moves upward within the large blue cluster. Understanding such configurations could provide more transparency into computer-driven learning of medical images. c-e Examples of t-SNE-based visualization and classification of test WSIs. For each prediction, we overlay 100 images patches extracted from testing images (represented by the red diamonds) to carry out classification. A k-nearest neighbor approach is used to assign individual tiles to clusters or undefined regions. In addition to qualitative visual predictions, the distribution of testing tiles (χ2 test) allows for quantitative statistically driven classification scores. Clinicopathological classes: schwannoma (c), glioma (d) and metastasis (e)

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