Figure 2.

Schematic representation of our DL system development. (i) Cytopathology slides obtained from fine-needle aspiration biopsies (FNABs) of UM tumors were digitized by whole-slide scanning at ×40 magnification. (ii) Slides were partitioned on a patient level for training and testing. (iii) Automatic high-quality ROI extraction. (iv) ResNet-50 architecture and dual-attention mechanism were used for training at an ROI level. (v) Downsampled, pixelwise features were extracted from every ROI and aggregated as slide-level features that were used as input to 2, 2-layer AANs to directly produce slide-level GEP prediction. ANN = artificial neural network; DL = deep learning; GEP = gene expression profile; ROI = region-of-interest; UM = uveal melanoma.