Fig. 1
From: Sparse coding of pathology slides compared to transfer learning with deep neural networks
Preprocessing of TCGA pathology slides. Full-extent low-resolution images were used to determine image coordinates; full-resolution image slices were used to generate sparse representations. Top: initial image; center: fast Fourier transform versus all-white, to determine optically dark regions of the image; bottom: non-overlapping image slices representing a succession of darkest remaining portions of the image. Full resolution regions of interest (ROIs; colored boxes) were extracted from the SVS file; the four darkest ROIs from each image were used for the analyses reported here