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  Indian J Med Microbiol
 

Figure 1: (a) Network architecture: Our deep learning framework consists of a fully convolutional ResNet-18 that processes a large field of view, along with two additional branches that process small field of views. The ResNet-18 backbone contains multiple shortcut connections. The dotted lines indicate shortcut connections where feature maps are also downsampled by 2. The small field-of-view branches emerge after the second convolutional block. The feature maps of the small field-of-view branches are downsampled by 8 to match the dimensions of the ResNet-18 feature map. These feature maps are concatenated before passing through a softmax output to produce a programmed death-ligand 1 staining probability map. (b) Model training: matching areas on Immunohistochemistry and H and E slides were annotated. The annotated regions of the H and E image were tiled into overlapping tiles (466 × 466 pixels) with a stride of 32 pixels, producing our training data. The multi-field-of-view ResNet-18 model was then trained using a cross-entropy loss function. The yellow square in the model schematic depicts the central region that is cropped for the small field of views. (c) Model inference: each image was divided into large nonoverlapping 4096 × 4096 input windows (blue dashed lines). Each large window was passed through the trained model. Because the model is fully convolutional, each tile within the large input window was processed in parallel, producing a 128 × 128 × 3 probability cube (the last dimension represents three classes). The resulting probability cubes were slotted into place and assembled to generate a probability map of the whole image. The class with the maximum probability was assigned to each tile

Figure 1: (a) Network architecture: Our deep learning framework consists of a fully convolutional ResNet-18 that processes a large field of view, along with two additional branches that process small field of views. The ResNet-18 backbone contains multiple shortcut connections. The dotted lines indicate shortcut connections where feature maps are also downsampled by 2. The small field-of-view branches emerge after the second convolutional block. The feature maps of the small field-of-view branches are downsampled by 8 to match the dimensions of the ResNet-18 feature map. These feature maps are concatenated before passing through a softmax output to produce a programmed death-ligand 1 staining probability map. (b) Model training: matching areas on Immunohistochemistry and H and E slides were annotated. The annotated regions of the H and E image were tiled into overlapping tiles (466 × 466 pixels) with a stride of 32 pixels, producing our training data. The multi-field-of-view ResNet-18 model was then trained using a cross-entropy loss function. The yellow square in the model schematic depicts the central region that is cropped for the small field of views. (c) Model inference: each image was divided into large nonoverlapping 4096 × 4096 input windows (blue dashed lines). Each large window was passed through the trained model. Because the model is fully convolutional, each tile within the large input window was processed in parallel, producing a 128 × 128 × 3 probability cube (the last dimension represents three classes). The resulting probability cubes were slotted into place and assembled to generate a probability map of the whole image. The class with the maximum probability was assigned to each tile