Fig. 2 | npj Digital Medicine

Fig. 2

From: Eliminating biasing signals in lung cancer images for prognosis predictions with deep learning

Fig. 2

Schematic overview of the proposed convolutional neural network architecture. The network receives two inputs: an image and the treatment indicator (\(t\)). Loss functions are depicted in double octagons. The last layer activations are used to separate factors of variation in the image. \({a}_{1}\) is trained to approximate the measurement of the collider \(x^\prime\). The rest of the last layer activations are constrained to be linearly independent from \(x^\prime\) through \({L}_{{\mathrm{reg}}}\). The total loss is \(L={L}_{y}+{L}_{x}+{L}_{{\mathrm{reg}}}\). CNN convolutional neural network.

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