A method for providing program logic adapted to remove artifacts from digital tissue images, the method comprising: - providing, for each of a plurality of different artifacts types, a respective artifact-generation logic configured for specifically generating said particular type of artifact; - generating, for each of a plurality of original images respectively depicting a tissue sample, a first artificially degraded image (210.A) by applying a first image-artifact-generation logic on each of the original images, the first image-artifact-generation logic being configured for specifically generating a first type of artifact, the first image-artifact-generation logics being configured for specifically generating a first type of artifact, the first image-artifact-generation logics being A) an image-acquisition-system-specific image-artifact-generation logic or B) a tissue-staining-artifact-generation logic; - applying, according to a first sequence, the different image-artifact-generation logics on each of the original images, whereby the image-artifact-generation logic at a first position within the first sequence takes each of the original images as input and wherein the degraded image output by said image-artifact-generation logic and by any image-artifact-generation logic at a subsequent position within the first sequence is used as input image by the next one of the different image-artifact-generation logics in the first sequence, thereby generating, for each of the original images, a first multi-fold degraded image (210.B); - generating a program logic configured for sequentially removing artifacts of each of the multiple different types from digital tissue images, the generation comprising: ∘ training machine-learning logics configured for removing artifacts of the multiple different types as to encode a multi-step-artifacts-removal logic, the training being performed on at least the original images and their respectively generated multi-fold degraded images (210.B); and ∘ using said trained multi-step-artifacts-removal logic as the program logic, wherein training the machine-learning logics further comprises: ▪ training, for each of the different artifacts types, an untrained version of a machine-learning logic that encodes a respective artifacts-removal logic on the original images and their respectively generated first artificially degraded images (210.A); ▪ returning each of the trained machine-learning logics as a component of the program logic. - providing, for each of a plurality of different artifacts types, a respective artifact-generation logic configured for specifically generating said particular type of artifact; - generating, for each of a plurality of original images respectively depicting a tissue sample, a first artificially degraded image (210.A) by applying a first image-artifact-generation logic on each of the original images, the first image-artifact-generation logic being configured for specifically generating a first type of artifact, the first image-artifact-generation logics being configured for specifically generating a first type of artifact, the first image-artifact-generation logics being A) an image-acquisition-system-specific image-artifact-generation logic or B) a tissue-staining-artifact-generation logic; - applying, according to a first sequence, the different image-artifact-generation logics on each of the original images, whereby the image-artifact-generation logic at a first position within the first sequence takes each of the original images as input and wherein the degraded image output by said image-artifact-generation logic and by any image-artifact-generation logic at a subsequent position within the first sequence is used as input image by the next one of the different image-artifact-generation logics in the first sequence, thereby generating, for each of the original images, a first multi-fold degraded image (210.B); - generating a program logic configured for sequentially removing artifacts of each of the multiple different types from digital tissue images, the generation comprising: ∘ training machine-learning logics configured for removing artifacts of the multiple different types as to encode a multi-step-artifacts-removal logic, the training being performed on at least the original images and their respectively generated multi-fold degraded images (210.B); and ∘ using said trained multi-step-artifacts-removal logic as the program logic, ∘ training machine-learning logics configured for removing artifacts of the multiple different types as to encode a multi-step-artifacts-removal logic, the training being performed on at least the original images and their respectively generated multi-fold degraded images (210.B); and ∘ using said trained multi-step-artifacts-removal logic as the program logic, ▪ training, for each of the different artifacts types, an untrained version of a machine-learning logic that encodes a respective artifacts-removal logic on the original images and their respectively generated first artificially degraded images (210.A); ▪ returning each of the trained machine-learning logics as a component of the program logic.