Fig. 1

(A) Given a whole slide image, our proposed AI iTIL scoring method first performs coarse semantic segmentation to identify broadly cancerous tissue. Within the cancerous region, one fine-grained model segments areas of tumour, stroma, and necrosis and another detects TILs. A summary iTIL score is calculated from the median quantification of TIL density associated with tumour regions, then thresholded to stratify patient outcome. (B) 512 × 512px regions fed to each model with different amounts of context, representing tissue areas of 2.048mm² at 4MPP, 256 µm² at 0.5 MPP, and 128 µm² at 0.25 MPP respectively. (C) Given an input image, our TIL model generates a heatmap consisting of circular, blob-like areas which correspond to the likelihood that each pixel belongs to a TIL. Using blob detection, we extract a set of point coordinates (shown with yellow dots) corresponding to TIL locations, which rejects the detection of TILs in areas of low likelihood (seen as faint blobs in the heatmap)