Abstract: In one embodiment, a method includes accessing a digital pathology image that depicts a tissue sample from a subject under a treatment, detecting tertiary lymphoid structures depicted within the digital pathology image of the tissue sample based on a machine-learning model, determining descriptive information associated with the detected tertiary lymphoid structures for the detected tertiary lymphoid structures, wherein the descriptive information comprises at least a maturation state associated with each of the detected tertiary lymphoid structures, and determining an outcome of the subject in response to the treatment based on the detected tertiary lymphoid structures and the descriptive information.

Abstract: Systems and methods relate to processing digital-pathology images. More specifically, aspects of the present disclosure are directed to accessing a whole-slide image depicting a slice of specimen, defining a set of tiles within at least part of the whole-slide image, generating one or more artifact prediction metrics by applying artifact detection to each tile of the set of tiles, wherein each of the one or more artifact prediction metrics corresponds to a predicted level of image quality of part or all of the whole-slide image, generating a quality heat map image corresponding to the whole-slide image, wherein the quality heat map image is based on the one or more artifact prediction metrics, and outputting the quality heat map image.