Test images were pre-processed before segmentation. A semantic UNet [11] was independently trained on the human colon tissue and cell line B training sets, using labels indicating not nuclei, but rather noise regions, consisting of out-of-focus nuclei or unbound fluorophores. The UNet was trained for 50 epochs at 300 steps per epoch, with a learning rate of 1e−4. Image augmentation, like that used for training our instance detection networks, was applied, along with the Adam optimizer (0.9 momentum) and binary cross entropy loss function. The trained models were then tested on their respective test sets, creating noise probability maps as the outputs (Fig. 9). The probability maps were binarized using Otsu’s method and subtracted from their respective test images, creating our denoised test sets.

Denoising super-resolution images using UNet. Noisy super-resolution image from our colon tissue dataset (a), noise probability map (b) output from a UNet model trained on similar noise regions from super-resolution images, and the denoised image (c), resulting from subtracting the noise map from the original image in (a)

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